Tag Archives: Multimodal Learning

LIMoE: Learning Multiple Modalities with One Sparse Mixture of Experts Model

Posted by Basil Mustafa, Research Software Engineer and Carlos Riquelme, Research Scientist, Google Research, Brain team

Sparse models stand out among the most promising approaches for the future of deep learning. Instead of every part of a model processing every input (“dense” modeling), sparse models employing conditional computation learn to route individual inputs to different “experts” in a potentially huge network. This has many benefits. First, model size can increase while keeping computational cost constant — an effective and environmentally friendlier way to scale models, which is often key to high performance. Sparsity also naturally compartmentalizes neural networks. Dense models that learn many different tasks simultaneously (multitask) or sequentially (continual learning) often suffer negative interference, where too much task variety means it is better to just train one model per task, or catastrophic forgetting, where the model becomes worse at earlier tasks as new ones are added. Sparse models help avoid both these phenomena — by not applying the whole model to all inputs, “experts” in the model can specialize on different tasks or data types while still taking advantage of shared parts of the model.

Research on sparsity has long been pursued at Google Research. Pathways summarizes the research vision of building one single large model that diligently handles thousands of tasks and numerous data modalities. So far there has been considerable progress in sparse unimodal models for language (Switch, Task-MoE, GLaM) and computer vision (Vision MoE). Today, we take another important step towards the Pathways vision by studying large sparse models that simultaneously handle images and text with modality-agnostic routing. A relevant approach is multimodal contrastive learning, which requires a solid understanding of both images and text in order to align pictures with their correct text description. The strongest models that tackle this task to date rely on independent networks for each modality (a “two-tower” approach).

In “Multimodal Contrastive Learning with LIMoE: the Language Image Mixture of Experts”, we present the first large-scale multimodal architecture using a sparse mixture of experts. It simultaneously processes both images and text, but uses sparsely activated experts that naturally specialize. On zero-shot image classification, LIMoE outperforms both comparable dense multimodal models and two-tower approaches. The largest LIMoE achieves 84.1% zero-shot ImageNet accuracy, comparable to more expensive state-of-the-art models. Sparsity enables LIMoE to scale up gracefully and learn to handle very different inputs, addressing the tension between being a jack-of-all-trades generalist and a master-of-one specialist.

The LIMoE architecture contains many “experts” and routers decide which tokens (parts of an image or sentence) go to which experts. After being processed by expert layers (gray) and shared dense layers (brown), a final output layer computes a single vector representation for either an image or a text.

Sparse Mixture of Expert Models
Transformers represent data as a sequence of vectors (or tokens). Though originally developed for text, they can be applied to most things that are representable as a sequence of tokens, e.g., images, videos, and audio. Recent large-scale MoE models add expert layers to the Transformer architecture (e.g., gShard and ST-MoE in natural language processing, and Vision MoE for vision tasks).

A standard Transformer consists of many “blocks”, each containing various different layers. One of these layers is a feed-forward network (FFN). For LIMoE and the works cited above, this single FFN is replaced by an expert layer that contains many parallel FFNs, each of which is an expert. Given a sequence of tokens to process, a simple router learns to predict which experts should handle which tokens. Only a small number of experts are activated per token, meaning although the model capacity is significantly increased by virtue of having so many experts, the actual computational cost is controlled by using them sparsely. If only one expert is activated, the model's cost is roughly equivalent to the standard Transformer model.

LIMoE does precisely that, activating one expert per example, thereby matching the computational cost of the dense baselines. What’s different is that the LIMoE router might see tokens of either image or text data.

A unique failure mode of MoE models occurs when they try to send all tokens to the same expert. Typically this is addressed with auxiliary losses, extra training objectives that encourage balanced expert usage. We found that dealing with multiple modalities interacted with sparsity to cause new failure modes that existing auxiliary losses could not address. To overcome this, we developed new auxiliary losses (more details in the paper) and used routing prioritization (BPR) during training, two innovations that resulted in stable and high performance multimodal models.

The new auxiliary losses (LIMoE aux) and routing prioritization (BPR) stabilized and improved overall performance (left) and increased the success rate of routing behavior (middle and right). A low success rate means the router does not use all the experts available and drops many tokens due to individual expert capacity being reached, which usually indicates the sparse model is not learning well. The combination introduced for LIMoE ensures high routing success rates for both images and text and consequently leads to significantly better performance.

Contrastive Learning with LIMoE
In multimodal contrastive learning, models are trained on paired image-text data (e.g., a photo and its caption). Typically, an image model extracts a representation of images, and a different text model extracts a representation of text. The contrastive learning objective encourages the image and text representations to be close for the same image-text pair and far away for content from different pairs. Such models with aligned representations can be adapted to new tasks without extra training data (“zero-shot”), e.g., an image will be classified as a dog if its representation is closer to the representation of the word “dog” than the word “cat”. This idea scales to thousands of classes and is referred to as zero-shot image classification.

CLIP and ALIGN (both two-tower models) scaled this process to achieve 76.2% and 76.4% zero-shot classification accuracy on the popular ImageNet dataset. We study one-tower models which compute both image and text representations. We find this reduces performance for dense models, likely due to negative interference or insufficient capacity. However, a compute-matched LIMoE not only improves over the one-tower dense model, but also outperforms two-tower dense models. We trained a series of models in a comparable training regimen to CLIP. Our dense L/16 model achieves 73.5% zero-shot accuracy, whereas LIMoE-L/16 gets to 78.6%, even outperforming CLIP’s more expensive, two-tower L/14 model (76.2%). As shown below, LIMoE’s use of sparsity provides a remarkable performance boost over dense models with equivalent cost.

For a given compute cost (x-axis), LIMoE models (circles, solid line) are significantly better than their dense baselines (triangles, dashed line). The architecture indicates the size of the underlying transformer, increasing from left (S/32) to right (L/16). Following standard convention, S (small), B (base), and L (large) refer to model scale. The number refers to the patch size, where smaller patches imply a larger architecture.

LiT and BASIC pushed zero-shot accuracy for dense two-tower models to 84.5% and 85.6% respectively. In addition to scaling, these approaches made use of specialized pre-training methods, repurposing image models that were already of exceptionally high quality. LIMoE-H/14 does not benefit from any pre-training or modality-specific components, but still achieved a comparable 84.1% zero-shot accuracy training from scratch. The scale of these models is also interesting to compare: LiT and BASIC are 2.1B and 3B parameter models. LIMoE-H/14 has 5.6B parameters in total, but via sparsity it only applies 675M parameters per token making it significantly more lightweight.

	Data seen during training
Model	Pre-training	Image-text	Total	Parameters per token	ImageNet accuracy
CLIP	-	12.8B	12.8B	~200M	76.2%
ALIGN	-	19.8B	19.8B	~410M	76.4%
LiT	25.8B	18.2B	44.0B	1.1B	84.5%
BASIC	19.7B	32.8B	52.5B	1.5B	85.6%
LIMoE H/14	-	23.3B	23.3B	675M	84.1%

Understanding LIMoE’s Behavior
LIMoE was motivated by the intuition that sparse conditional computation enables a generalist multimodal model to still develop the specialization needed to excel at understanding each modality. We analyzed LIMoE’s expert layers and uncovered a few interesting phenomena.

First, we see the emergence of modality-specialized experts. In our training setup there are many more image tokens than text tokens, so all experts tend to process at least some images, but some experts process either mostly images, mostly text, or both.

Distributions for an eight expert LIMoE; percentages indicate the amount of image tokens processed by the expert. There are one or two experts clearly specialized on text (shown by the mostly blue experts), usually two to four image specialists (mostly red), and the remainder are somewhere in the middle.

There are also some clear qualitative patterns among the image experts — e.g., in most LIMoE models, there is an expert that processes all image patches that contain text. In the example below, one expert processes fauna and greenery, and another processes human hands.

LIMoE chooses an expert for each token. Here we show which image tokens go to which experts on one of the layers of LIMoE-H/14. Despite not being trained to do so, we observe the emergence of semantic experts that specialize in specific topics such as plants or wheels.

Moving Forward
Multimodal models that handle many tasks are a promising route forward, and there are two key ingredients for success: scale, and the ability to avoid interference between distinct tasks and modalities while taking advantage of synergies. Sparse conditional computation is an excellent way of doing both. It enables performant and efficient generalist models that also have the capacity and flexibility for the specialization necessary to excel at individual tasks, as demonstrated by LIMoE’s solid performance with less compute.

Acknowledgements
We thank our co-authors on this work: Joan Puigcerver, Rodolphe Jenatton and Neil Houlsby. We also thank Andreas Steiner, Xiao Wang and Xiaohua Zhai, who led early explorations into dense single-tower models for contrastive multimodal learning, and also were instrumental in providing data access. We enjoyed useful discussions with André Susano Pinto, Maxim Neumann, Barret Zoph, Liam Fedus, Wei Han and Josip Djolonga. Finally, we would also like to thank and acknowledge Tom Small for the awesome animated figure used in this post.

Source: Google AI Blog

End-to-end Generative Pre-training for Multimodal Video Captioning

Posted by Paul Hongsuck Seo and Arsha Nagrani, Research Scientists, Google Research, Perception Team

Multimodal video captioning systems utilize both the video frames and speech to generate natural language descriptions (captions) of videos. Such systems are stepping stones towards the longstanding goal of building multimodal conversational systems that effortlessly communicate with users while perceiving environments through multimodal input streams.

Unlike video understanding tasks (e.g., video classification and retrieval) where the key challenge lies in processing and understanding multimodal input videos, the task of multimodal video captioning includes the additional challenge of generating grounded captions. The most widely adopted approach for this task is to train an encoder-decoder network jointly using manually annotated data. However, due to a lack of large-scale, manually annotated data, the task of annotating grounded captions for videos is labor intensive and, in many cases, impractical. Previous research such as VideoBERT and CoMVT pre-train their models on unlabelled videos by leveraging automatic speech recognition (ASR). However, such models often cannot generate natural language sentences because they lack a decoder, and thus only the video encoder is transferred to the downstream tasks.

In “End-to-End Generative Pre-training for Multimodal Video Captioning”, published at CVPR 2022, we introduce a novel pre-training framework for multimodal video captioning. This framework, which we call multimodal video generative pre-training or MV-GPT, jointly trains a multimodal video encoder and a sentence decoder from unlabelled videos by leveraging a future utterance as the target text and formulating a novel bi-directional generation task. We demonstrate that MV-GPT effectively transfers to multimodal video captioning, achieving state-of-the-art results on various benchmarks. Additionally, the multimodal video encoder is competitive for multiple video understanding tasks, such as VideoQA, text-video retrieval, and action recognition.

Future Utterance as an Additional Text Signal
Typically, each training video clip for multimodal video captioning is associated with two different texts: (1) a speech transcript that is aligned with the clip as a part of the multimodal input stream, and (2) a target caption, which is often manually annotated. The encoder learns to fuse information from the transcript with visual contents, and the target caption is used to train the decoder for generation. However, in the case of unlabelled videos, each video clip comes only with a transcript from ASR, without a manually annotated target caption. Moreover, we cannot use the same text (the ASR transcript) for the encoder input and decoder target, since the generation of the target would then be trivial.

MV-GPT circumvents this challenge by leveraging a future utterance as an additional text signal and enabling joint pre-training of the encoder and decoder. However, training a model to generate future utterances that are often not grounded in the input content is not ideal. So we apply a novel bi-directional generation loss to reinforce the connection to the input.

Bi-directional Generation Loss
The issue of non-grounded text generation is mitigated by formulating a bi-directional generation loss that includes forward and backward generation. Forward generation produces future utterances given visual frames and their corresponding transcripts and allows the model to learn to fuse the visual content with its corresponding transcript. Backward generation takes the visual frames and future utterances to train the model to generate a transcript that contains more grounded text of the video clip. Bi-directional generation loss in MV-GPT allows the encoder and the decoder to be trained to handle visually grounded texts.

Bi-directional generation in MV-GPT. A model is trained with two generation losses. In forward generation, the model generates a future utterance (blue boxes) given the frames and the present utterance (red boxes), whereas the present is generated from the future utterance in backward generation. Two special beginning-of-sentence tokens ([BOS-F] and [BOS-B]) initiate forward and backward generation for the decoder.

Results on Multimodal Video Captioning
We compare MV-GPT to existing pre-training losses using the same model architecture, on YouCook2 with standard evaluation metrics (Bleu-4, Cider, Meteor and Rouge-L). While all pre-training techniques improve captioning performances, it is critical to pre-train the decoder jointly to improve model performance. We demonstrate that MV-GPT outperforms the previous state-of-the-art joint pre-training method by over 3.5% with relative gains across all four metrics.

Pre-training Loss	Pre-trained Parts	Bleu-4	Cider	Meteor	Rouge-L
No Pre-training	N/A	13.25	1.03	17.56	35.48
CoMVT	Encoder	14.46	1.24	18.46	37.17
UniVL	Encoder + Decoder	19.95	1.98	25.27	46.81
MV-GPT (ours)	Encoder + Decoder	21.26	2.14	26.36	48.58


MV-GPT performance across four metrics (Bleu-4, Cider, Meteor and Rouge-L) of different pre-training losses on YouCook2. “Pre-trained parts” indicates which parts of the model are pre-trained — only the encoder or both the encoder and decoder. We reimplement the loss functions of existing methods but use our model and training strategies for a fair comparison.

We transfer a model pre-trained by MV-GPT to four different captioning benchmarks: YouCook2, MSR-VTT, ViTT and ActivityNet-Captions. Our model achieves state-of-the-art performance on all four benchmarks by significant margins. For instance on the Meteor metric, MV-GPT shows over 12% relative improvements in all four benchmarks.

	YouCook2	MSR-VTT	ViTT	ActivityNet-Captions
Best Baseline	22.35	29.90	11.00	10.90
MV-GPT (ours)	27.09	38.66	26.75	12.31


Meteor metric scores of the best baseline methods and MV-GPT on four benchmarks.

Results on Non-generative Video Understanding Tasks
Although MV-GPT is designed to train a generative model for multimodal video captioning, we also find that our pre-training technique learns a powerful multimodal video encoder that can be applied to multiple video understanding tasks, including VideoQA, text-video retrieval and action classification. When compared to the best comparable baseline models, the model transferred from MV-GPT shows superior performance in five video understanding benchmarks on their primary metrics — i.e., top-1 accuracy for VideoQA and action classification benchmarks, and recall at 1 for the retrieval benchmark.

Task	Benchmark	Best Comparable Baseline	MV-GPT
VideoQA	MSRVTT-QA	41.5	41.7
VideoQA	ActivityNet-QA	38.9	39.1
Text-Video Retrieval	MSR-VTT	33.7	37.3
Action Recognition	Kinetics-400	78.9	80.4
Action Recognition	Kinetics-600	80.6	82.4


Comparisons of MV-GPT to best comparable baseline models on five video understanding benchmarks. For each dataset we report the widely used primary metric, i.e., MSRVTT-QA and ActivityNet-QA: Top-1 answer accuracy; MSR-VTT: Recall at 1; and Kinetics: Top-1 classification accuracy.

Summary
We introduce MV-GPT, a new generative pre-training framework for multimodal video captioning. Our bi-directional generative objective jointly pre-trains a multimodal encoder and a caption decoder by using utterances sampled at different times in unlabelled videos. Our pre-trained model achieves state-of-the-art results on multiple video captioning benchmarks and other video understanding tasks, namely VideoQA, video retrieval and action classification.

Acknowledgements
This research was conducted by Paul Hongsuck Seo, Arsha Nagrani, Anurag Arnab and Cordelia Schmid.

Source: Google AI Blog

Image-Text Pre-training with Contrastive Captioners

Posted by Zirui Wang and Jiahui Yu, Research Scientists, Google Research, Brain Team

Oftentimes, machine learning (ML) model developers begin their design using a generic backbone model that is trained at scale and with capabilities transferable to a wide range of downstream tasks. In natural language processing, a number of popular backbone models, including BERT, T5, GPT-3 (sometimes also referred to as “foundation models”), are pre-trained on web-scale data and have demonstrated generic multi-tasking capabilities through zero-shot, few-shot or transfer learning. Compared with training over-specialized individual models, pre-training backbone models for a large number of downstream tasks can amortize the training costs, allowing one to overcome resource limitations when building large scale models.

In computer vision, pioneering work has shown the effectiveness of single-encoder models pre-trained for image classification to capture generic visual representations that are effective for other downstream tasks. More recently, contrastive dual-encoder (CLIP, ALIGN, Florence) and generative encoder-decoder (SimVLM) approaches trained using web-scale noisy image-text pairs have been explored. Dual-encoder models exhibit remarkable zero-shot image classification capabilities but are less effective for joint vision-language understanding. On the other hand, encoder-decoder methods are good at image captioning and visual question answering but cannot perform retrieval-style tasks.

In “CoCa: Contrastive Captioners are Image-Text Foundation Models”, we present a unified vision backbone model called Contrastive Captioner (CoCa). Our model is a novel encoder-decoder approach that simultaneously produces aligned unimodal image and text embeddings and joint multimodal representations, making it flexible enough to be directly applicable for all types of downstream tasks. Specifically, CoCa achieves state-of-the-art results on a series of vision and vision-language tasks spanning vision recognition, cross-modal alignment, and multimodal understanding. Furthermore, it learns highly generic representations so that it can perform as well or better than fully fine-tuned models with zero-shot learning or frozen encoders.

Overview of Contrastive Captioners (CoCa) compared to single-encoder, dual-encoder and encoder-decoder models.

Method
We propose CoCa, a unified training framework that combines contrastive loss and captioning loss on a single training data stream consisting of image annotations and noisy image-text pairs, effectively merging single-encoder, dual-encoder and encoder-decoder paradigms.

To this end, we present a novel encoder-decoder architecture where the encoder is a vision transformer (ViT), and the text decoder transformer is decoupled into two parts, a unimodal text decoder and a multimodal text decoder. We skip cross-attention in unimodal decoder layers to encode text-only representations for contrastive loss, and cascade multimodal decoder layers with cross-attention to image encoder outputs to learn multimodal image-text representations for captioning loss. This design maximizes the model's flexibility and universality in accommodating a wide spectrum of tasks, and at the same time, it can be efficiently trained with a single forward and backward propagation for both training objectives, resulting in minimal computational overhead. Thus, the model can be trained end-to-end from scratch with training costs comparable to a naïve encoder-decoder model.

Illustration of forward propagation used by CoCa for both contrastive and captioning losses.

Benchmark Results
The CoCa model can be directly fine-tuned on many tasks with minimal adaptation. By doing so, our model achieves a series of state-of-the-art results on popular vision and multimodal benchmarks, including (1) visual recognition: ImageNet, Kinetics-400/600/700, and MiT; (2) cross-modal alignment: MS-COCO, Flickr30K, and MSR-VTT; and (3) multimodal understanding: VQA, SNLI-VE, NLVR2, and NoCaps.

Comparison of CoCa with other image-text backbone models (without task-specific customization) and multiple state-of-the-art task-specialized models.

It is noteworthy that CoCa attains these results as a single model adapted for all tasks while often lighter than prior top-performing specialized models. For example, CoCa obtains 91.0% ImageNet top-1 accuracy while using less than half the parameters of prior state-of-the-art models. In addition, CoCa also obtains strong generative capability of high-quality image captions.

Image classification scaling performance comparing fine-tuned ImageNet top-1 accuracy versus model size.

Text captions generated by CoCa with NoCaps images as input.

Zero-Shot Performance
Besides achieving excellent performance with fine-tuning, CoCa also outperforms previous state-of-the-art models on zero-shot learning tasks, including image classification,and cross-modal retrieval. CoCa obtains 86.3% zero-shot accuracy on ImageNet while also robustly outperforming prior models on challenging variant benchmarks, such as ImageNet-A, ImageNet-R, ImageNet-V2, and ImageNet-Sketch. As shown in the figure below, CoCa obtains better zero-shot accuracy with smaller model sizes compared to prior methods.

Image classification scaling performance comparing zero-shot ImageNet top-1 accuracy versus model size.

Frozen Encoder Representation
One particularly exciting observation is that CoCa achieves results comparable to the best fine-tuned models using only a frozen visual encoder, in which features extracted after model training are used to train a classifier, rather than the more computationally intensive effort of fine-tuning a model. On ImageNet, a frozen CoCa encoder with a learned classification head obtains 90.6% top-1 accuracy, which is better than the fully fine-tuned performance of existing backbone models (90.1%). We also find this setup to work extremely well for video recognition. We feed sampled video frames into the CoCa frozen image encoder individually, and fuse output features by attentional pooling before applying a learned classifier. This simple approach using a CoCa frozen image encoder achieves video action recognition top-1 accuracy of 88.0% on Kinetics-400 dataset and demonstrates that CoCa learns a highly generic visual representation with the combined training objectives.

Comparison of Frozen CoCa visual encoder with (multiple) best-performing fine-tuned models.

Conclusion
We present Contrastive Captioner (CoCa), a novel pre-training paradigm for image-text backbone models. This simple method is widely applicable to many types of vision and vision-language downstream tasks, and obtains state-of-the-art performance with minimal or even no task-specific adaptations.

Acknowledgements
We would like to thank our co-authors Vijay Vasudevan, Legg Yeung, Mojtaba Seyedhosseini, and Yonghui Wu who have been involved in all aspects of the project. We also would like to thank Yi-Ting Chen, Kaifeng Chen, Ye Xia, Zhen Li, Chao Jia, Yinfei Yang, Zhengdong Zhang, Wei Han, Yuan Cao, Tao Zhu, Futang Peng, Soham Ghosh, Zihang Dai, Xin Li, Anelia Angelova, Jason Baldridge, Izhak Shafran, Shengyang Dai, Abhijit Ogale, Zhifeng Chen, Claire Cui, Paul Natsev, Tom Duerig for helpful discussions, Andrew Dai for help with contrastive models, Christopher Fifty and Bowen Zhang for help with video models, Yuanzhong Xu for help with model scaling, Lucas Beyer for help with data preparation, Andy Zeng for help with MSR-VTT evaluation, Hieu Pham and Simon Kornblith for help with zero-shot evaluations, Erica Moreira and Victor Gomes for help with resource coordination, Liangliang Cao for proofreading, Tom Small for creating the animations used in this blogpost, and others in the Google Brain team for support throughout this project.

Source: Google AI Blog

Locked-image Tuning: Adding Language Understanding to Image Models

Posted by Andreas Steiner and Basil Mustafa, Research Software Engineers at Google Research, Brain team

The ability to classify images into categories has been transformed by deep learning. It has also been significantly accelerated by transfer learning, whereby models are first pre-trained on large datasets, like ImageNet, to learn visual representations that are then transferred via fine-tuning to a new task with less data (e.g., classifying animals). Previous works such as BiT and ViT employed these methods to achieve state-of-the-art performance on a wide range of classification tasks, such as the VTAB benchmark.

However, fine-tuning has some downsides: though pre-training is done only once, fine-tuning is necessary on every new dataset for which task-specific data is needed. Multimodal contrastive learning is an alternative, recently popularized paradigm (e.g., CLIP, ALIGN) that overcomes these issues by instead learning how to match free-form text with images. These models can then solve new tasks by reformulating them as image-text matching problems, without extra data (referred to as “zero-shot” learning). Contrastive learning is flexible and easy to adapt to new tasks, but has its own limitations, namely the need for a lot of paired image-text data and weaker performance than transfer learning approaches.

With those limitations in mind, we propose “LiT: Zero-Shot Transfer with Locked-image Text Tuning”, to appear at CVPR 2022. LiT models learn to match text to an already pre-trained image encoder. This simple yet effective setup provides the best of both worlds: strong image representations from pre-training, plus flexible zero-shot transfer to new tasks via contrastive learning. LiT achieves state-of-the-art zero-shot classification accuracy, significantly closing the gap between the two styles of learning. We think the best way to understand is to try it yourself, so we’ve included a demo of LiT models at the end of this post.

Fine-tuning (left) requires task-specific data and training to adapt a pre-trained model to a new task. An LiT model (right) can be used with any task, without further data or adaptation.

Contrastive Learning on Image-Text Data
Contrastive learning models learn representations from “positive” and “negative” examples, such that representations for "positive" examples are similar to each other but different from "negative" examples.

Multimodal contrastive learning applies this to pairs of images and associated texts. An image encoder computes representations from images, and a text encoder does the same for texts. Each image representation is encouraged to be close to the representation of its associated text (“positive”), but distinct from the representation of other texts ("negatives") in the data, and vice versa. This has typically been done with randomly initialized models (“from scratch”), meaning the encoders have to simultaneously learn representations and how to match them.

Multimodal contrastive learning trains models to produce similar representations for closely matched images and texts.

This training can be done on noisy, loosely aligned pairs of image and text, which naturally occur on the web. This circumvents the need for manual labeling, and makes data scaling easy. Furthermore, the model learns much richer visual concepts — it’s not constrained to what’s defined in the classification label space. Instead of classifying an image as “coffee”, it can understand whether it’s "a small espresso in a white mug” or “a large latte in a red flask”.

Once trained, a model that aligns image and text can be used in many ways. For zero-shot classification, we compare image representations to text representations of the class names. For example, a “wombat vs jaguar” classifier can be built by computing the representations of the texts “jaguar” and “wombat”, and classifying an image as a jaguar if its representation better matches the former. This approach scales to thousands of classes and makes it very easy to solve classification tasks without the extra data necessary for fine-tuning. Another application of contrastive models is image search (a.k.a. image-text retrieval), by finding the image whose representation best matches that of a given text, or vice versa.

The Best of Both Worlds with Locked-image Tuning
As mentioned earlier, transfer learning achieves state-of-the-art accuracy, but requires per-task labels, datasets, and training. On the other hand, contrastive models are flexible, scalable, and easily adaptable to new tasks, but fall short in performance. To compare, at the time of writing, the state of the art on ImageNet classification using transfer learning is 90.94%, but the best contrastive zero-shot models achieve 76.4%.

LiT tuning bridges this gap: we contrastively train a text model to compute representations well aligned with the powerful ones available from a pre-trained image encoder. Importantly, for this to work well, the image encoder should be “locked“, that is: it should not be updated during training. This may be unintuitive since one usually expects the additional information from further training to increase performance, but we find that locking the image encoder consistently leads to better results.

LiT-tuning contrastively trains a text encoder to match a pre-trained image encoder. The text encoder learns to compute representations that align to those from the image encoder.

This can be considered an alternative to the classic fine-tuning stage, where the image encoder is separately adapted to every new classification task; instead we have one stage of LiT-tuning, after which the model can classify any data. LiT-tuned models achieve 84.5% zero-shot accuracy on ImageNet classification, showing significant improvements over previous methods that train models from scratch, and halving the performance gap between fine-tuning and contrastive learning.

Left: LiT-tuning significantly closes the gap between the best contrastive models and the best models fine-tuned with labels. Right: Using a pre-trained image encoder is always helpful, but locking it is surprisingly a key part of the recipe to success; unlocked image models (dashed) yield significantly worse performance.

An impressive benefit of contrastive models is increased robustness — they retain high accuracy on datasets that typically fool fine-tuned models, such as ObjectNet and ImageNet-C. Similarly, LiT-tuned models have high performance across various challenging versions of ImageNet, for example achieving a state-of-the-art 81.1% accuracy on ObjectNet.

LiT-tuning has other advantages. While prior contrastive works require large amounts of data and train for a very long time, the LiT approach is much less data hungry. LiT models trained on 24M publicly available image-text pairs rival the zero-shot classification performance of prior models trained on 400M image-text pairs of private data. The locked image encoder also leads to faster training with a smaller memory footprint. On larger datasets, image representations can be pre-computed; not running the image model during training further improves efficiency and also unlocks much larger batch sizes, which increases the number of “negatives” the model sees and is key to high-performance contrastive learning. The method works well with varied forms of image pre-training (e.g., including self-supervised learning), and with many publicly available image models. We hope that these benefits make LiT a great testbed for researchers.

Conclusion
We present Locked-image Tuning (LiT), which contrastively trains a text encoder to match image representations from a powerful pre-trained image encoder. This simple method is data and compute efficient, and substantially improves zero-shot classification performance compared to existing contrastive learning approaches.

Want to try it yourself?

A preview of the demo: use it to match free-form text descriptions to images and build your own zero-shot classifier!

We have prepared a small interactive demo to try some LiT-tuned models. We also provide a Colab with more advanced use cases and larger models, which are a great way to get started.

Acknowledgments
We would like to thank Xiaohua Zhai, Xiao Wang, Daniel Keysers, Alexander Kolesnikov, and Lucas Beyer who have co-authored the LiT paper and been involved in all aspects of its development, as well as the Brain team in Zürich. We also would like to thank Tom Small for creating the animations used in this blogpost.

Source: Google AI Blog

Multimodal Bottleneck Transformer (MBT): A New Model for Modality Fusion

Posted by Arsha Nagrani and Chen Sun, Research Scientists, Google Research, Perception Team

People interact with the world through multiple sensory streams (e.g., we see objects, hear sounds, read words, feel textures and taste flavors), combining information and forming associations between senses. As real-world data consists of various signals that co-occur, such as video frames and audio tracks, web images and their captions and instructional videos and speech transcripts, it is natural to apply a similar logic when building and designing multimodal machine learning (ML) models.

Effective multimodal models have wide applications — such as multilingual image retrieval, future action prediction, and vision-language navigation — and are important for several reasons; robustness, which is the ability to perform even when one or more modalities is missing or corrupted, and complementarity between modalities, which is the idea that some information may be present only in one modality (e.g., audio stream) and not in the other (e.g., video frames). While the dominant paradigm for multimodal fusion, called late fusion, consists of using separate models to encode each modality, and then simply combining their output representations at the final step, investigating how to effectively and efficiently combine information from different modalities is still understudied.

In “Attention Bottlenecks for Multimodal Fusion”, published at NeurIPS 2021, we introduce a novel transformer-based model for multimodal fusion in video called Multimodal Bottleneck Transformer (MBT). Our model restricts cross-modal attention flow between latent units in two ways: (1) through tight fusion bottlenecks, that force the model to collect and condense the most relevant inputs in each modality (sharing only necessary information with other modalities), and (2) to later layers of the model, allowing early layers to specialize to information from individual modalities. We demonstrate that this approach achieves state-of-the-art results on video classification tasks, with a 50% reduction in FLOPs compared to a vanilla multimodal transformer model. We have also released our code as a tool for researchers to leverage as they expand on multimodal fusion work.

A Vanilla Multimodal Transformer Model
Transformer models consistently obtain state-of-the-art results in ML tasks, including video (ViViT) and audio classification (AST). Both ViViT and AST are built on the Vision Transformer (ViT); in contrast to standard convolutional approaches that process images pixel-by-pixel, ViT treats an image as a sequence of patch tokens (i.e., tokens from a smaller part, or patch, of an image that is made up of multiple pixels). These models then perform self-attention operations across all pairs of patch tokens. However, using transformers for multimodal fusion is challenging because of their high computational cost, with complexity scaling quadratically with input sequence length.

Because transformers effectively process variable length sequences, the simplest way to extend a unimodal transformer, such as ViT, to the multimodal case is to feed the model a sequence of both visual and auditory tokens, with minimal changes to the transformer architecture. We call this a vanilla multimodal transformer model, which allows free attention flow (called vanilla cross-attention) between different spatial and temporal regions in an image, and across frequency and time in audio inputs, represented by spectrograms. However, while easy to implement by concatenating audio and video input tokens, vanilla cross-attention at all layers of the transformer model is unnecessary because audio and visual inputs contain dense, fine-grained information, which may be redundant for the task — increasing complexity.

Restricting Attention Flow
The issue of growing complexity for long sequences in multimodal models can be mitigated by reducing the attention flow. We restrict attention flow using two methods, specifying the fusion layer and adding attention bottlenecks.

Fusion layer (early, mid or late fusion): In multimodal models, the layer where cross-modal interactions are introduced is called the fusion layer. The two extreme versions are early fusion (where all layers in the transformer are cross-modal) and late fusion (where all layers are unimodal and no cross-modal information is exchanged in the transformer encoder). Specifying a fusion layer in between leads to mid fusion. This technique builds on a common paradigm in multimodal learning, which is to restrict cross-modal flow to later layers of the network, allowing early layers to specialize in learning and extracting unimodal patterns.
Attention bottlenecks: We also introduce a small set of latent units that form an attention bottleneck (shown below in purple), which force the model, within a given layer, to collate and condense information from each modality before sharing it with the other, while still allowing free attention flow within a modality. We demonstrate that this bottlenecked version (MBT), outperforms or matches its unrestricted counterpart with lower computational cost.

The different attention configurations in our model. Unlike late fusion (top left), where no cross-modal information is exchanged in the transformer encoder, we investigate two pathways for the exchange of cross-modal information. Early and mid fusion (top middle, top right) is done via standard pairwise self attention across all hidden units in a layer. For mid fusion, cross-modal attention is applied only to later layers in the model. Bottleneck fusion (bottom left) restricts attention flow within a layer through tight latent units called attention bottlenecks. Bottleneck mid fusion (bottom right) applies both forms of restriction in conjunction for optimal performance.

Bottlenecks and Computation Cost
We apply MBT to the task of sound classification using the AudioSet dataset and investigate its performance for two approaches: (1) vanilla cross-attention, and (2) bottleneck fusion. For both approaches, mid fusion (shown by the middle values of the x-axis below) outperforms both early (fusion layer = 0) and late fusion (fusion layer = 12). This suggests that the model benefits from restricting cross-modal connections to later layers, allowing earlier layers to specialize in learning unimodal features; however, it still benefits from multiple layers of cross-modal information flow. We find that adding attention bottlenecks (bottleneck fusion) outperforms or maintains performance with vanilla cross-attention for all fusion layers, with more prominent improvements at lower fusion layers.

The impact of using attention bottlenecks for fusion on mAP performance (left) and compute (right) at different fusion layers on AudioSet. Attention bottlenecks (red) improve performance over vanilla cross-attention (blue) at lower computational cost. Mid fusion, which is in fusion layers 4-10, outperforms both early (fusion layer = 0) and late (fusion layer = 12) fusion, with best performance at fusion layer 8.

We compare the amount of computation, measured in GFLOPs, for both vanilla cross-attention and bottleneck fusion. Using a small number of attention bottlenecks (four bottleneck tokens used in our experiments) adds negligible extra computation over a late fusion model, with computation remaining largely constant with varying fusion layers. This is in contrast to vanilla cross-attention, which has a non-negligible computational cost for every layer it is applied to. We note that for early fusion, bottleneck fusion outperforms vanilla cross-attention by over 2 mean average precision points (mAP) on audiovisual sound classification, with less than half the computational cost.

Results on Sound Classification and Action Recognition
MBT outperforms previous research on popular video classification tasks — sound classification (AudioSet and VGGSound) and action recognition (Kinetics and Epic-Kitchens). For multiple datasets, late fusion and MBT with mid fusion (both fusing audio and vision) outperform the best single modality baseline, and MBT with mid fusion outperforms late fusion.

Across multiple datasets, fusing audio and vision outperforms the best single modality baseline, and MBT with mid fusion outperforms late fusion. For each dataset we report the widely used primary metric, i.e., Audioset: mAP, Epic-Kitchens: Top-1 action accuracy, VGGSound, Moments-in-Time and Kinetics: Top-1 classification accuracy.

Visualization of Attention Heatmaps
To understand the behavior of MBT, we visualize the attention computed by our network following the attention rollout technique. We compute heat maps of the attention from the output classification tokens to the image input space for a vanilla cross-attention model and MBT on the AudioSet test set. For each video clip, we show the original middle frame on the left with the ground truth labels overlayed at the bottom. We demonstrate that the attention is particularly focused on regions in the images that contain motion and create sound, e.g., the fingertips on the piano, the sewing machine, and the face of the dog. The fusion bottlenecks in MBT further force the attention to be localized to smaller regions of the images, e.g., the mouth of the dog in the top left and the woman singing in the middle right. This provides some evidence that the tight bottlenecks force MBT to focus only on the image patches that are relevant for an audio classification task and that benefit from mid fusion with audio.

Summary
We introduce MBT, a new transformer-based architecture for multimodal fusion, and explore various fusion approaches using cross-attention between bottleneck tokens. We demonstrate that restricting cross-modal attention via a small set of fusion bottlenecks achieves state-of-the-art results on a number of video classification benchmarks while also reducing computational costs compared to vanilla cross-attention models.

Acknowledgements
This research was conducted by Arsha Nagrani, Anurag Arnab, Shan Yang, Aren Jansen, Cordelia Schmid and Chen Sun. The blog post was written by Arsha Nagrani, Anurag Arnab and Chen Sun. Animations were created by Tom Small.

Source: Google AI Blog

4D-Net: Learning Multi-Modal Alignment for 3D and Image Inputs in Time

Posted by AJ Piergiovanni and Anelia Angelova, Research Scientists, Google Research

While not immediately obvious, all of us experience the world in four dimensions (4D). For example, when walking or driving down the street we observe a stream of visual inputs, snapshots of the 3D world, which, when taken together in time, creates a 4D visual input. Today’s autonomous vehicles and robots are able to capture much of this information through various onboard sensing mechanisms, such as LiDAR and cameras.

LiDAR is a ubiquitous sensor that uses light pulses to reliably measure the 3D coordinates of objects in a scene, however, it is also sparse and has a limited range — the farther one is from a sensor, the fewer points will be returned. This means that far-away objects might only get a handful of points, or none at all, and might not be seen by LiDAR alone. At the same time, images from the onboard camera, which is a dense input, are incredibly useful for semantic understanding, such as detecting and segmenting objects. With high resolution, cameras can be very effective at detecting objects far away, but are less accurate in measuring the distance.

Autonomous vehicles collect data from both LiDAR and onboard camera sensors. Each sensor measurement is recorded at regular time intervals, providing an accurate representation of the 4D world. However, very few research algorithms use both of these in combination, especially when taken “in time”, i.e., as a temporally ordered sequence of data, mostly due to two major challenges. When using both sensing modalities simultaneously, 1) it is difficult to maintain computational efficiency, and 2) pairing the information from one sensor to another adds further complexity since there is not always a direct correspondence between LiDAR points and onboard camera RGB image inputs.

In “4D-Net for Learned Multi-Modal Alignment”, published at ICCV 2021, we present a neural network that can process 4D data, which we call 4D-Net. This is the first attempt to effectively combine both types of sensors, 3D LiDAR point clouds and onboard camera RGB images, when both are in time. We also introduce a dynamic connection learning method, which incorporates 4D information from a scene by performing connection learning across both feature representations. Finally, we demonstrate that 4D-Net is better able to use motion cues and dense image information to detect distant objects while maintaining computational efficiency.

4D-Net
In our scenario, we use 4D inputs (3D point clouds and onboard camera image data in time) to solve a very popular visual understanding task, the 3D box detection of objects. We study the question of how one can combine the two sensing modalities, which come from different domains and have features that do not necessarily match — i.e., sparse LiDAR inputs span the 3D space and dense camera images only produce 2D projections of a scene. The exact correspondence between their respective features is unknown, so we seek to learn the connections between these two sensor inputs and their feature representations. We consider neural network representations where each of the feature layers can be combined with other potential layers from other sensor inputs, as shown below.

4D-Net effectively combines 3D LiDAR point clouds in time with RGB images, also streamed in time as video, learning the connections between different sensors and their feature representations.

Dynamic Connection Learning Across Sensing Modalities
We use a light-weight neural architecture search to learn the connections between both types of sensor inputs and their feature representations, to obtain the most accurate 3D box detection. In the autonomous driving domain it is especially important to reliably detect objects at highly variable distances, with modern LiDAR sensors reaching several hundreds of meters in range. This implies that more distant objects will appear smaller in the images and the most valuable features for detecting them will be in earlier layers of the network, which better capture fine-scale features, as opposed to close-by objects represented by later layers. Based on this observation, we modify the connections to be dynamic and select among features from all layers using self-attention mechanisms. We apply a learnable linear layer, which is able to apply attention-weighting to all other layer weights and learn the best combination for the task at hand.

Connection learning approach schematic, where connections between features from the 3D point cloud inputs are combined with the features from the RGB camera video inputs. Each connection learns the weighting for the corresponding inputs.

Results
We evaluate our results against state-of-the-art approaches on the Waymo Open Dataset benchmark, for which previous models have only leveraged 3D point clouds in time or a combination of a single point cloud and camera image data. 4D-Net uses both sensor inputs efficiently, processing 32 point clouds in time and 16 RGB frames within 164 milliseconds, and performs well compared to other methods. In comparison, the next best approach is less efficient and accurate because its neural net computation takes 300 milliseconds, and uses fewer sensor inputs than 4D-Net.

Results on a 3D scene. Top: 3D boxes, corresponding to detected vehicles, are shown in different colors; dotted line boxes are for objects that were missed. Bottom: The boxes are shown in the corresponding camera images for visualization purposes.

Detecting Far-Away Objects
Another benefit of 4D-Net is that it takes advantage of both the high resolution provided by RGB, which can accurately detect objects on the image plane, and the accurate depth that the point cloud data provides. As a result, objects at a greater distance that were previously missed by point cloud-only approaches can be detected by a 4D-Net. This is due to the fusion of camera data, which is able to detect distant objects, and efficiently propagate this information to the 3D part of the network to produce accurate detections.

Is Data in Time Valuable?
To understand the value of the 4D-Net, we perform a series of ablation studies. We find that substantial improvements in detection accuracy are obtained if at least one of the sensor inputs is streamed in time. Considering both sensor inputs in time provides the largest improvements in performance.

4D-Net performance for 3D object detection measured in average precision (AP) when using point clouds (PC), Point Clouds in Time (PC + T), RGB image inputs (RGB) and RGB images in Time (RGB + T). Combining both sensor inputs in time is best (rightmost columns in blue) compared to the left-most columns (green) which use a PC without RGB inputs. All joint methods use our 4D-Net multi-modal learning.

Multi-stream 4D-Net
Since the 4D-Net dynamic connection learning mechanism is general, we are not limited to only combining a point cloud stream with an RGB video stream. In fact, we find that it is very cost-effective to provide a large resolution single-image stream, and a low-resolution video stream in conjunction with 3D point cloud stream inputs. Below, we demonstrate examples of a four-stream architecture, which performs better than the two-stream one with point clouds in time and images in time.

Dynamic connection learning selects specific feature inputs to connect together. With multiple input streams, 4D-Net has to learn connections between multiple target feature representations, which is straightforward as the algorithm does not change and simply selects specific features from the union of inputs. This is an incredibly light-weight process that uses a differentiable architecture search, which can discover new wiring within the model architecture itself and thus effectively find new 4D-Net models.

Example multi-stream 4D-Net which consists of a stream of 3D point clouds in time (PC+T), and multiple image streams: a high-resolution single image stream, a medium-resolution single image stream and a video stream (of even lower resolution) images.

Summary
While deep learning has made tremendous advances in real-life applications, the research community is just beginning to explore learning from multiple sensing modalities. We present 4D-Net which learns how to combine 3D point clouds in time and RGB camera images in time, for the popular application of 3D object detection in autonomous driving. We demonstrate that 4D-Net is an effective approach for detecting objects, especially at distant ranges. We hope this work will provide researchers with a valuable resource for future 4D data research.

Acknowledgements
This work is done by AJ Piergiovanni, Vincent Casser, Michael Ryoo and Anelia Angelova. We thank our collaborators, Vincent Vanhoucke, Dragomir Anguelov and our colleagues at Waymo and Robotics at Google for their support and discussions. We also thank Tom Small for the graphics animation.

Source: Google AI Blog

MURAL: Multimodal, Multi-task Retrieval Across Languages

Posted by Aashi Jain, AI Resident and Yinfei Yang, Staff Research Scientist, Google Research

For many concepts, there is no direct one-to-one translation from one language to another, and even when there is, such translations often carry different associations and connotations that are easily lost for a non-native speaker. In such cases, however, the meaning may be more obvious when grounded in visual examples. Take, for instance, the word "wedding". In English, one often associates a bride in a white dress and a groom in a tuxedo, but when translated into Hindi (शादी), a more appropriate association may be a bride wearing vibrant colors and a groom wearing a sherwani. What each person associates with the word may vary considerably, but if they are shown an image of the intended concept, the meaning becomes more clear.

The word “wedding” in English and Hindi conveys different mental images. Images are taken from wikipedia, credited to Psoni2402 (left) and David McCandless (right) with CC BY-SA 4.0 license.

With current advances in neural machine translation and image recognition, it is possible to reduce this sort of ambiguity in translation by presenting a text paired with a supporting image. Prior research has made much progress in learning image–text joint representations for high-resource languages, such as English. These representation models strive to encode the image and text into vectors in a shared embedding space, such that the image and the text describing it are close to each other in that space. For example, ALIGN and CLIP have shown that training a dual-encoder model (i.e., one trained with two separate encoders) on image–text pairs using a contrastive learning loss works remarkably well when provided with ample training data.

Unfortunately, such image–text pair data does not exist at the same scale for the majority of languages. In fact, more than 90% of this type of web data belongs to the top-10 highly-resourced languages, such as English and Chinese, with much less data for under-resourced languages. To overcome this issue, one could either try to manually collect image–text pair data for under-resourced languages, which would be prohibitively difficult due to the scale of the undertaking, or one could seek to leverage pre-existing datasets (e.g., translation pairs) that could inform the necessary learned representations for multiple languages.

In “MURAL: Multimodal, Multitask Representations Across Languages”, presented at Findings of EMNLP 2021, we describe a representation model for image–text matching that uses multitask learning applied to image–text pairs in combination with translation pairs covering 100+ languages. This technology could allow users to express words that may not have a direct translation into a target language using images instead. For example, the word “valiha”, refers to a type of tube zither played by the Malagasy people, which lacks a direct translation into most languages, but could be easily described using images. Empirically, MURAL shows consistent improvements over state-of-the-art models, other benchmarks, and competitive baselines across the board. Moreover, MURAL does remarkably well for the majority of the under-resourced languages on which it was tested. Additionally, we discover interesting linguistic correlations learned by MURAL representations.

MURAL Architecture
The MURAL architecture is based on the structure of ALIGN, but employed in a multitask fashion. Whereas ALIGN uses a dual-encoder architecture to draw together representations of images and associated text descriptions, MURAL employs the dual-encoder structure for the same purpose while also extending it across languages by incorporating translation pairs. The dataset of image–text pairs is the same as that used for ALIGN, and the translation pairs are those used for LaBSE.

MURAL solves two contrastive learning tasks: 1) image–text matching and 2) text–text (bitext) matching, with both tasks sharing the text encoder module. The model learns associations between images and text from the image–text data, and learns the representations of hundreds of diverse languages from the translation pairs. The idea is that a shared encoder will transfer the image–text association learned from high-resource languages to under-resourced languages. We find that the best model employs an EfficientNet-B7 image encoder and a BERT-large text encoder, both trained from scratch. The learned representation can be used for downstream visual and vision-language tasks.

The architecture of MURAL depicts dual encoders with a shared text-encoder between the two tasks trained using a contrastive learning loss.

Multilingual Image-to-Text and Text-to-Image Retrieval
To demonstrate MURAL’s capabilities, we choose the task of cross-modal retrieval (i.e., retrieving relevant images given a text and vice versa) and report the scores on various academic image–text datasets covering well-resourced languages, such as MS-COCO (and its Japanese variant, STAIR), Flickr30K (in English) and Multi30K (extended to German, French, Czech), XTD (test-only set with seven well-resourced languages: Italian, Spanish, Russian, Chinese, Polish, Turkish, and Korean). In addition to well-resourced languages, we also evaluate MURAL on the recently published Wikipedia Image–Text (WIT) dataset, which covers 108 languages, with a broad range of both well-resourced (English, French, Chinese, etc.) and under-resourced (Swahili, Hindi, etc.) languages.

MURAL consistently outperforms prior state-of-the-art models, including M3P, UC2, and ALIGN, in both zero-shot and fine-tuned settings evaluated on well-resourced and under-resourced languages. We see remarkable performance gains for under-resourced languages when compared to the state-of-the-art model, ALIGN.

Mean recall on various multilingual image–text retrieval benchmarks. Mean recall is a common metric used to evaluate cross-modal retrieval performance on image–text datasets (higher is better). It measures the Recall@N (i.e., the chance that the ground truth image appears in the first N retrieved images) averaged over six measurements: Image→Text and Text→Image retrieval for N=[1, 5, 10]. Note that XTD scores report Recall@10 for Text→Image retrieval.

Retrieval Analysis
We also analyzed zero-shot retrieved examples on the WIT dataset comparing ALIGN and MURAL for English (en) and Hindi (hi). For under-resourced languages like Hindi, MURAL shows improved retrieval performance compared to ALIGN that reflects a better grasp of the text semantics.

Comparison of the top-5 images retrieved by ALIGN and by MURAL for the Text→Image retrieval task on the WIT dataset for the Hindi text, एक तश्तरी पर बिना मसाले या सब्ज़ी के रखी हुई सादी स्पगॅत्ती”, which translates to the English, “A bowl containing plain noodles without any spices or vegetables”.

Even for Image→Text retrieval in a well-resourced language, like French, MURAL shows better understanding for some words. For example, MURAL returns better results for the query “cadran solaire” (“sundial”, in French) than ALIGN, which doesn’t retrieve any text describing sundials (below).

Comparison of the top-5 text results from ALIGN and from MURAL on the Image→Text retrieval task for the same image of a sundial.

Embeddings Visualization
Previously, researchers have shown that visualizing model embeddings can reveal interesting connections among languages — for instance, representations learned by a neural machine translation (NMT) model have been shown to form clusters based on their membership to a language family. We perform a similar visualization for a subset of languages belonging to the Germanic, Romance, Slavic, Uralic, Finnic, Celtic, and Finno-Ugric language families (widely spoken in Europe and Western Asia). We compare MURAL’s text embeddings with LaBSE’s, which is a text-only encoder.

A plot of LabSE’s embeddings shows distinct clusters of languages influenced by language families. For instance, Romance languages (in purple, below) fall into a different region than Slavic languages (in brown, below). This finding is consistent with prior work that investigates intermediate representations learned by a NMT system.

Visualization of text representations of LaBSE for 35 languages. Languages are color coded based on their genealogical association. Representative languages include: Germanic (red) — German, English, Dutch; Uralic (orange) — Finnish, Estonian; Slavic (brown) — Polish, Russian; Romance (purple) — Italian, Portuguese, Spanish; Gaelic (blue) — Welsh, Irish.

In contrast to LaBSE’s visualization, MURAL’s embeddings, which are learned with a multimodal objective, shows some clusters that are in line with areal linguistics (where elements are shared by languages or dialects in a geographic area) and contact linguistics (where languages or dialects interact and influence each other). Notably, in the MURAL embedding space, Romanian (ro) is closer to the Slavic languages like Bulgarian (bg) and Macedonian (mk), which is in line with the Balkan sprachbund, than it is in LaBSE. Another possible language contact brings Finnic languages, Estonian (et) and Finnish (fi), closer to the Slavic languages cluster. The fact that MURAL pivots on images as well as translations appears to add an additional view on language relatedness as learned in deep representations, beyond the language family clustering observed in a text-only setting.

Visualization of text representations of MURAL for 35 languages. Color coding is the same as the figure above.

Final Remarks
Our findings show that training jointly using translation pairs helps overcome the scarcity of image–text pairs for many under-resourced languages and improves cross-modal performance. Additionally, it is interesting to observe hints of areal linguistics and contact linguistics in the text representations learned by using a multimodal model. This warrants more probing into different connections learned implicitly by multimodal models, such as MURAL. Finally, we hope this work promotes further research in the multimodal, multilingual space where models learn representations of and connections between languages (expressed via images and text), beyond well-resourced languages.

Acknowledgements
This research is in collaboration with Mandy Guo, Krishna Srinivasan, Ting Chen, Sneha Kudugunta, Chao Jia, and Jason Baldridge. We thank Zarana Parekh, Orhan Firat, Yuqing Chen, Apu Shah, Anosh Raj, Daphne Luong, and others who provided feedback for the project. We are also grateful for general support from Google Research teams.

Source: Google AI Blog

Making Better Future Predictions by Watching Unlabeled Videos

Posted by Dave Epstein, Student Researcher and Chen Sun, Staff Research Scientist, Google Research

Machine learning (ML) agents are increasingly deployed in the real world to make decisions and assist people in their daily lives. Making reasonable predictions about the future at varying timescales is one of the most important capabilities for such agents because it enables them to predict changes in the world around them, including other agents’ behaviors, and plan how to act next. Importantly, successful future prediction requires both capturing meaningful transitions in the environment (e.g., dough transforming into bread) and adapting to how transitions unfold over time in order to make decisions.

Previous work in future prediction from visual observations has largely been constrained by the format of its output (e.g., pixels that represent an image) or a manually-defined set of human activities (e.g., predicting if someone will keep walking, sit down, or jump). These are either too detailed and hard to predict or lack important information about the richness of the real world. For example, predicting “person jumping” does not capture why they’re jumping, what they’re jumping onto, etc. Also, with very few exceptions, previous models were designed to make predictions at a fixed offset into the future, which is a limiting assumption because we rarely know when meaningful future states will happen.

For example, in a video about making ice cream (depicted below), the meaningful transition from “cream” to “ice cream” occurs over 35 seconds, so models predicting such transitions would need to look 35 seconds ahead. But this time interval varies a large amount across different activities and videos — meaningful transitions occur at any distance into the future. Learning to make such predictions at flexible intervals is hard because the desired ground truth may be relatively ambiguous. For example, the correct prediction could be the just-churned ice cream in the machine, or scoops of the ice cream in a bowl. In addition, collecting such annotations at scale (i.e., frame-by-frame for millions of videos) is infeasible. However, many existing instructional videos come with speech transcripts, which often offer concise, general descriptions throughout entire videos. This source of data can guide a model’s attention toward important parts of the video, obviating the need for manual labeling and allowing a flexible, data-driven definition of the future.

In “Learning Temporal Dynamics from Cycles in Narrated Video”, published at ICCV 2021, we propose an approach that is self-supervised, using a recent large unlabeled dataset of diverse human action. The resulting model operates at a high level of abstraction, can make predictions arbitrarily far into the future, and chooses how far into the future to predict based on context. Called Multi-Modal Cycle Consistency (MMCC), it leverages narrated instructional video to learn a strong predictive model of the future. We demonstrate how MMCC can be applied, without fine-tuning, to a variety of challenging tasks, and qualitatively examine its predictions. In the example below, MMCC predicts the future (d) from present frame (a), rather than less relevant potential futures (b) or (c).

This work uses cues from vision and language to predict high-level changes (such as cream becoming ice cream) in video (video from HowTo100M).

Viewing Videos as Graphs
The foundation of our method is to represent narrated videos as graphs. We view videos as a collection of nodes, where nodes are either video frames (sampled at 1 frame per second) or segments of narrated text (extracted with automatic speech recognition systems), encoded by neural networks. During training, MMCC constructs a graph from the nodes, using cross-modal edges to connect video frames and text segments that refer to the same state, and temporal edges to connect the present (e.g., strawberry-flavored cream) and the future (e.g., soft-serve ice cream). The temporal edges operate on both modalities equally — they can start from either a video frame, some text, or both, and can connect to a future (or past) state in either modality. MMCC achieves this by learning a latent representation shared by frames and text and then making predictions in this representation space.

Multi-modal Cycle Consistency
To learn the cross-modal and temporal edge functions without supervision, we apply the idea of cycle consistency. Here, cycle consistency refers to the construction of cycle graphs, in which the model constructs a series of edges from an initial node to other nodes and back again: Given a start node (e.g., a sample video frame), the model is expected to find its cross-modal counterpart (i.e., text describing the frame) and combine them as the present state. To do this, at the start of training, the model assumes that frames and text with the same timestamps are counterparts, but then relaxes this assumption later. The model then predicts a future state, and the node most similar to this prediction is selected. Finally, the model attempts to invert the above steps by predicting the present state backward from the future node, and thus connecting the future node back with the start node.

The discrepancy between the model’s prediction of the present from the future and the actual present is the cycle-consistency loss. Intuitively, this training objective requires the predicted future to contain enough information about its past to be invertible, leading to predictions that correspond to meaningful changes to the same entities (e.g., tomato becoming marinara sauce, or flour and eggs in a bowl becoming dough). Moreover, the inclusion of cross-modal edges ensures future predictions are meaningful in either modality.

To learn the temporal and cross-modal edge functions end-to-end, we use the soft attention technique, which first outputs how likely each node is to be the target node of the edge, and then “picks” a node by taking the weighted average among all possible candidates. Importantly, this cyclic graph constraint makes few assumptions for the kind of temporal edges the model should learn, as long as they end up forming a consistent cycle. This enables the emergence of long-term temporal dynamics critical for future prediction without requiring manual labels of meaningful changes.

An example of the training objective: A cycle graph is expected to be constructed between the chicken with soy sauce and the chicken in chili oil because they are two adjacent steps in the chicken’s preparation (video from HowTo100M).

Discovering Cycles in Real-World Video
MMCC is trained without any explicit ground truth, using only long video sequences and randomly sampled starting conditions (a frame or text excerpt) and asking the model to find temporal cycles. After training, MMCC can identify meaningful cycles that capture complex changes in video.

Given frames as input (left), MMCC selects relevant text from video narrations and uses both modalities to predict a future frame (middle). It then finds text relevant to this future and uses it to predict the past (right). Using its knowledge of how objects and scenes change over time, MMCC “closes the cycle” and ends up where it started (videos from HowTo100M).

The model can also start from narrated text rather than frames and still find relevant transitions (videos from HowTo100M).

Zero-Shot Applications
For MMCC to identify meaningful transitions over time in an entire video, we define a “likely transition score” for each pair (A, B) of frames in a video, according to the model's predictions — the closer B is to our model’s prediction of the future of A, the higher the score assigned. We then rank all pairs according to this score and show the highest-scoring pairs of present and future frames detected in previously unseen videos (examples below).

The highest-scoring pairs from eight random videos, which showcase the versatility of the model across a wide range of tasks (videos from HowTo100M).

We can use this same approach to temporally sort an unordered collection of video frames without any fine-tuning by finding an ordering that maximizes the overall confidence scores between all adjacent frames in the sorted sequence.

Left: Shuffled frames from three videos. Right: MMCC unshuffles the frames. The true order is shown under each frame. Even when MMCC does not predict the ground truth, its predictions often appear reasonable, and so, it can present an alternate ordering (videos from HowTo100M).

Evaluating Future Prediction
We evaluate the model’s ability to anticipate action, potentially minutes in advance, using the top-k recall metric, which here measures a model’s ability to retrieve the correct future (higher is better). On CrossTask, a dataset of instruction videos with labels describing key steps, MMCC outperforms the previous self-supervised state-of-the-art models in inferring possible future actions.

	Recall
Model	Top-1	Top-5	Top-10
Cross-modal	2.9	14.2	24.3
Repr. Ant.	3.0	13.3	26.0
MemDPC	2.9	15.8	27.4
TAP	4.5	17.1	27.9
MMCC	5.4	19.9	33.8

Conclusions
We have introduced a self-supervised method to learn temporal dynamics by cycling through narrated instructional videos. Despite the simplicity of the model’s architecture, it can discover meaningful long-term transitions in vision and language, and can be applied without further training to challenging downstream tasks, such as anticipating far-away action and ordering collections of images. An interesting future direction is transferring the model to agents so they can use it to conduct long-term planning.

Acknowledgements
The core team includes Dave Epstein, Jiajun Wu, Cordelia Schmid, and Chen Sun. We thank Alexei Efros, Mia Chiquier, and Shiry Ginosar for their feedback, and Allan Jabri for inspiration in figure design. Dave would like to thank Dídac Surís and Carl Vondrick for insightful early discussions on cycling through time in video.

Source: Google AI Blog

SimVLM: Simple Visual Language Model Pre-training with Weak Supervision

Posted by Zirui Wang, Student Researcher and Yuan Cao Research Scientist, Google Research, Brain Team

Vision-language modeling grounds language understanding in corresponding visual inputs, which can be useful for the development of important products and tools. For example, an image captioning model generates natural language descriptions based on its understanding of a given image. While there are various challenges to such cross-modal work, significant progress has been made in the past few years on vision-language modeling thanks to the adoption of effective vision-language pre-training (VLP). This approach aims to learn a single feature space from both visual and language inputs, rather than learning two separate feature spaces, one each for visual inputs and another for language inputs. For this purpose, existing VLP often leverages an object detector, like Faster R-CNN, trained on labeled object detection datasets to isolate regions-of-interest (ROI), and relies on task-specific approaches (i.e., task-specific loss functions) to learn representations of images and texts jointly. Such approaches require annotated datasets or time to design task-specific approaches, and so, are less scalable.

To address this challenge, in “SimVLM: Simple Visual Language Model Pre-training with Weak Supervision”, we propose a minimalist and effective VLP, named SimVLM, which stands for “Simple Visual Language Model”. SimVLM is trained end-to-end with a unified objective, similar to language modeling, on a vast amount of weakly aligned image-text pairs (i.e., the text paired with an image is not necessarily a precise description of the image). The simplicity of SimVLM enables efficient training on such a scaled dataset, which helps the model to achieve state-of-the-art performance across six vision-language benchmarks. Moreover, SimVLM learns a unified multimodal representation that enables strong zero-shot cross-modality transfer without fine-tuning or with fine-tuning only on text data, including for tasks such as open-ended visual question answering, image captioning and multimodal translation.

Model and Pre-training Procedure
Unlike existing VLP methods that adopt pre-training procedures similar to masked language modeling (like in BERT), SimVLM adopts the sequence-to-sequence framework and is trained with a one prefix language model (PrefixLM) objective, which receives the leading part of a sequence (the prefix) as inputs, then predicts its continuation. For example, given the sequence “A dog is chasing after a yellow ball”, the sequence is randomly truncated to “A dog is chasing” as the prefix, and the model will predict its continuation. The concept of a prefix similarly applies to images, where an image is divided into a number of “patches”, then a subset of those patches are sequentially fed to the model as inputs—this is called an “image patch sequence”. In SimVLM, for multimodal inputs (e.g., images and their captions), the prefix is a concatenation of both the image patch sequence and prefix text sequence, received by the encoder. The decoder then predicts the continuation of the textual sequence. Compared to prior VLP models combining several pre-training losses, the PrefixLM loss is the only training objective and significantly simplifies the training process. This approach for SimVLM maximizes its flexibility and universality in accommodating different task setups.

Finally, due to its success for both language and vision tasks, like BERT and ViT, we adopt the Transformer architecture as the backbone of our model, which, unlike prior ROI-based VLP approaches, enables the model to directly take in raw images as inputs. Moreover, inspired by CoAtNet, we adopt a convolution stage consisting of the first three blocks of ResNet in order to extract contextualized patches, which we find more advantageous than the naïve linear projection in the original ViT model. The overall model architecture is illustrated below.

Overview of the SimVLM model architecture.

The model is pre-trained on large-scale web datasets for both image-text and text-only inputs. For joint vision and language data, we use the training set of ALIGN which contains about 1.8B noisy image-text pairs. For text-only data, we use the Colossal Clean Crawled Corpus (C4) dataset introduced by T5, totaling 800G web-crawled documents.

Benchmark Results
After pre-training, we fine-tune our model on the following multimodal tasks: VQA, NLVR2, SNLI-VE, COCO Caption, NoCaps and Multi30K En-De. For example, for VQA the model takes an image and corresponding questions about the input image, and generates the answer as output. We evaluate SimVLM models of three different sizes (base: 86M parameters, large: 307M and huge: 632M) following the same setup as in ViT. We compare our results with strong existing baselines, including LXMERT, VL-T5, UNITER, OSCAR, Villa, SOHO, UNIMO, VinVL, and find that SimVLM achieves state-of-the-art performance across all these tasks despite being much simpler.

	VQA		NLVR2		SNLI-VE		CoCo Caption
Model	test-dev	test-std	dev	test-P	dev	test	B@4	M	C	S

LXMERT	72.4	72.5	74.9	74.5	-	-	-	-	-	-
VL-T5	-	70.3	74.6	73.6	-	-	-	-	116.5	-
UNITER	73.8	74	79.1	80	79.4	79.4	-	-	-	-
OSCAR	73.6	73.8	79.1	80.4	-	-	41.7	30.6	140	24.5
Villa	74.7	74.9	79.8	81.5	80.2	80	-	-	-	-
SOHO	73.3	73.5	76.4	77.3	85	85	-	-	-	-
UNIMO	75.1	75.3	-	-	81.1	80.6	39.6	-	127.7	-
VinVL	76.6	76.6	82.7	84	-	-	41	31.1	140.9	25.2

SimVLM base	77.9	78.1	81.7	81.8	84.2	84.2	39	32.9	134.8	24
SimVLM large	79.3	79.6	84.1	84.8	85.7	85.6	40.3	33.4	142.6	24.7
SimVLM huge	80	80.3	84.5	85.2	86.2	86.3	40.6	33.7	143.3	25.4

Evaluation results on a subset of 6 vision-language benchmarks in comparison with existing baseline models. Metrics used above (higher is better): BLEU-4 (B@4), METEOR (M), CIDEr (C), SPICE (S). Similarly, evaluation on NoCaps and Multi30k En-De also show state-of-the-art performance.

Zero-Shot Generalization
Since SimVLM has been trained on large amounts of data from both visual and textual modalities, it is interesting to ask whether it is capable of performing zero-shot cross-modality transfer. We examine the model on multiple tasks for this purpose, including image captioning, multilingual captioning, open-ended VQA and visual text completion. We take the pre-trained SimVLM and directly decode it for multimodal inputs with fine-tuning only on text data or without fine-tuning entirely. Some examples are given in the figure below. It can be seen that the model is able to generate not only high-quality image captions, but also German descriptions, achieving cross-lingual and cross-modality transfer at the same time.

Examples of SimVLM zero-shot generalization. (a) Zero-shot image captioning: Given an image together with text prompts, the pre-trained model predicts the content of the image without fine-tuning. (b) zero-shot cross-modality transfer on German image captioning: The model generates captions in German even though it has never been fine-tuned on image captioning data in German. (c) Generative VQA: The model is capable of generating answers outside the candidates of the original VQA dataset. (d) Zero-shot visual text completion: The pre-trained model completes a textual description grounded on the image contents; (e) Zero-shot open-ended VQA: The model provides factual answers to the questions about images, after continued pre-training on the WIT dataset. Images are from NoCaps, which come from the Open Images dataset under the CC BY 2.0 license.

To quantify SimVLM’s zero-shot performance, we take the pre-trained, frozen model and decode it on the COCO Caption and NoCaps benchmarks, then compare with supervised baselines. Even without supervised fine-tuning (in the middle-rows), SimVLM can reach zero-shot captioning quality close to the quality of supervised methods.

Zero shot image captioning results. Here “Pre.” indicates the model is pre-trained and “Sup.” means the model is finetuned on task-specific supervision. For NoCaps, [In, Near, Out] refer to in-domain, near-domain and out-of-domain respectively. We compare results from BUTD, AoANet, M2 Transformer, OSCAR and VinVL. Metrics used above (higher is better): BLEU-4 (B@4), METEOR (M), CIDEr (C), SPICE (S). For NoCaps, CIDEr numbers are reported.

Conclusion
We propose a simple yet effective framework for VLP. Unlike prior work using object detection models and task-specific auxiliary losses, our model is trained end-to-end with a single prefix language model objective. On various vision-language benchmarks, this approach not only obtains state-of-the-art performance, but also exhibits intriguing zero-shot behaviors in multimodal understanding tasks.

Acknowledgements
We would like to thank Jiahui Yu, Adams Yu, Zihang Dai, Yulia Tsvetkov for preparation of the SimVLM paper, Hieu Pham, Chao Jia, Andrew Dai, Bowen Zhang, Zhifeng Chen, Ruoming Pang, Douglas Eck, Claire Cui and Yonghui Wu for helpful discussions, Krishna Srinivasan, Samira Daruki, Nan Du and Aashi Jain for help with data preparation, Jonathan Shen, Colin Raffel and Sharan Narang for assistance on experimental settings, and others on the Brain team for support throughout this project.

Source: Google AI Blog

Announcing WIT: A Wikipedia-Based Image-Text Dataset

Posted by Krishna Srinivasan, Software Engineer and Karthik Raman, Research Scientist, Google Research

Multimodal visio-linguistic models rely on rich datasets in order to model the relationship between images and text. Traditionally, these datasets have been created by either manually captioning images, or crawling the web and extracting the alt-text as the caption. While the former approach tends to result in higher quality data, the intensive manual annotation process limits the amount of data that can be created. On the other hand, the automated extraction approach can lead to bigger datasets, but these require either heuristics and careful filtering to ensure data quality or scaling-up models to achieve strong performance. An additional shortcoming of existing datasets is the dearth of coverage in non-English languages. This naturally led us to ask: Can one overcome these limitations and create a high-quality, large-sized, multilingual dataset with a variety of content?

Today we introduce the Wikipedia-Based Image Text (WIT) Dataset, a large multimodal dataset, created by extracting multiple different text selections associated with an image from Wikipedia articles and Wikimedia image links. This was accompanied by rigorous filtering to only retain high quality image-text sets. As detailed in “WIT: Wikipedia-based Image Text Dataset for Multimodal Multilingual Machine Learning”, presented at SIGIR ‘21, this resulted in a curated set of 37.5 million entity-rich image-text examples with 11.5 million unique images across 108 languages. The WIT dataset is available for download and use under the Creative Commons license. We are also excited to announce that we are hosting a competition with the WIT dataset in Kaggle in collaboration with Wikimedia Research and other external collaborators.

Dataset	Images	Text	Contextual Text	Languages
Flickr30K	32K	158K	-	< 8
SBU Captions	1M	1M	-	1
MS-COCO	330K	1.5M	-	< 4; 7 (test only)
CC-3M CC-12M	3.3M 12M	3.3M 12M	- -	1 1
WIT	11.5M	37.5M	~119M	108

WIT’s increased language coverage and larger size relative to previous datasets.

The unique advantages of the WIT dataset are:

Size: WIT is the largest multimodal dataset of image-text examples that is publicly available.
Multilingual: With 108 languages, WIT has 10x or more languages than any other dataset.
Contextual information: Unlike typical multimodal datasets, which have only one caption per image, WIT includes many page-level and section-level contextual information.
Real world entities: Wikipedia, being a broad knowledge-base, is rich with real world entities that are represented in WIT.
Challenging test set: In our recent work accepted at EMNLP, all state-of-the-art models demonstrated significantly lower performance on WIT vs. traditional evaluation sets (e.g., ~30 point drop in recall).

Generating the Dataset
The main goal of WIT was to create a large dataset without sacrificing on quality or coverage of concepts. Thus, we started by leveraging the largest online encyclopedia available today: Wikipedia.

For an example of the depth of information available, consider the Wikipedia page for Half Dome (Yosemite National Park, CA). As shown below, the article has numerous interesting text captions and relevant contextual information for the image, such as the page title, main page description, and other contextual information and metadata.

Example wikipedia page with various image-associated text selections and contexts we can extract. From the Wikipedia page for Half Dome : Photo by DAVID ILIFF. License: CC BY-SA 3.0.

Example of the Wikipedia page for this specific image of Half Dome. From the Wikipedia page for Half Dome : Photo by DAVID ILIFF. License: CC BY-SA 3.0.

We started by selecting Wikipedia pages that have images, then extracted various image-text associations and surrounding contexts. To further refine the data, we performed a rigorous filtering process to ensure data quality. This included text-based filtering to ensure caption availability, length and quality (e.g., by removing generic default filler text); image-based filtering to ensure each image is a certain size with permissible licensing; and finally, image-and-text-entity–based filtering to ensure suitability for research (e.g., excluding those classified as hate speech). We further randomly sampled image-caption sets for evaluation by human editors, who overwhelmingly agreed that 98% of the samples had good image-caption alignment.

Highly Multilingual
With data in 108 languages, WIT is the first large-scale, multilingual, multimodal dataset.

# of Image-Text Sets	Unique Languages	# of Images	Unique Languages
> 1M	9	> 1M	6
500K - 1M	10	500K - 1M	12
100K - 500K	36	100K - 500K	35
50K - 100K	15	50K - 100K	17
14K - 50K	38	13K - 50K	38

WIT: coverage statistics across languages.

Example of an image that is present in more than a dozen Wikipedia pages across >12 languages. From the Wikipedia page for Wolfgang Amadeus Mozart.

The First Contextual Image-Text Dataset
Most multimodal datasets only offer a single text caption (or multiple versions of a similar caption) for the given image. WIT is the first dataset to provide contextual information, which can help researchers model the effect of context on image captions as well as the choice of images.

WIT dataset example showing image-text data and additional contextual information.

In particular, key textual fields of WIT that may be useful for research include:

Text captions: WIT offers three different kinds of image captions. This includes the (potentially context influenced) “Reference description”, the (likely context independent) “Attribution description” and “Alt-text description”.
Contextual information: This includes the page title, page description, URL and local context about the Wikipedia section including the section title and text.

WIT has broad coverage across these different fields, as shown below.

Image-Text Fields of WIT	Train	Val	Test	Total / Unique
Rows / Tuples	37.1M	261.8K	210.7K	37.6M
Unique Images	11.4M	58K	57K	11.5M
Reference Descriptions	16.9M	150K	104K	17.2M / 16.7M
Attribution Descriptions	34.8M	193K	200K	35.2M / 10.9M
Alt-Text	5.3M	29K	29K	5.4M / 5.3M
Context Texts	-	-	-	119.8M

Key fields of WIT include both text captions and contextual information.

A High-Quality Training Set and a Challenging Evaluation Benchmark
The broad coverage of diverse concepts in Wikipedia means that the WIT evaluation sets serve as a challenging benchmark, even for state-of-the-art models. We found that for image-text retrieval, the mean recall scores for traditional datasets were in the 80s, whereas for the WIT test set, it was in the 40s for well-resourced languages and in the 30s for the under-resourced languages. We hope this in turn can help researchers to build stronger, more robust models.

WIT Dataset and Competition with Wikimedia and Kaggle
Additionally, we are happy to announce that we are partnering with Wikimedia Research and a few external collaborators to organize a competition with the WIT test set. We are hosting this competition in Kaggle. The competition is an image-text retrieval task. Given a set of images and text captions, the task is to retrieve the appropriate caption(s) for each image.

To enable research in this area, Wikipedia has kindly made available images at 300-pixel resolution and a Resnet-50–based image embeddings for most of the training and the test dataset. Kaggle will be hosting all this image data in addition to the WIT dataset itself and will provide colab notebooks. Further, the competitors will have access to a discussion forum in Kaggle in order to share code and collaborate. This enables anyone interested in multimodality to get started and run experiments easily. We are excited and looking forward to what will result from the WIT dataset and the Wikipedia images in the Kaggle platform.

Conclusion
We believe that the WIT dataset will aid researchers in building better multimodal multilingual models and in identifying better learning and representation techniques, ultimately leading to improved Machine Learning models in real-world tasks over visio-linguistic data. For any questions, please contact [email protected]. We would love to hear about how you are using the WIT dataset.

Acknowledgements
We would like to thank our co-authors in Google Research: Jiecao Chen, Michael Bendersky and Marc Najork. We thank Beer Changpinyo, Corinna Cortes, Joshua Gang, Chao Jia, Ashwin Kakarla, Mike Lee, Zhen Li, Piyush Sharma, Radu Soricut, Ashish Vaswani, Yinfei Yang, and our reviewers for their insightful feedback and comments.

We thank Miriam Redi and Leila Zia from Wikimedia Research for collaborating with us on the competition and providing image pixels and image embedding data. We thank Addison Howard and Walter Reade for helping us host this competition in Kaggle. We also thank Diane Larlus (Naver Labs Europe (NLE)), Yannis Kalantidis (NLE), Stéphane Clinchant (NLE), Tiziano Piccardi Ph.D. student at EPFL, Lucie-Aimée Kaffee PhD student at University of Southampton and Yacine Jernite (Hugging Face) for their valuable contribution towards the competition.

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Tag Archives: Multimodal Learning

LIMoE: Learning Multiple Modalities with One Sparse Mixture of Experts Model

Source: Google AI Blog

End-to-end Generative Pre-training for Multimodal Video Captioning

Source: Google AI Blog

Image-Text Pre-training with Contrastive Captioners

Source: Google AI Blog

Locked-image Tuning: Adding Language Understanding to Image Models

Source: Google AI Blog

Multimodal Bottleneck Transformer (MBT): A New Model for Modality Fusion

Source: Google AI Blog

4D-Net: Learning Multi-Modal Alignment for 3D and Image Inputs in Time

Source: Google AI Blog

MURAL: Multimodal, Multi-task Retrieval Across Languages

Source: Google AI Blog

Making Better Future Predictions by Watching Unlabeled Videos

Source: Google AI Blog

SimVLM: Simple Visual Language Model Pre-training with Weak Supervision

Source: Google AI Blog

Announcing WIT: A Wikipedia-Based Image-Text Dataset

Source: Google AI Blog