Hi everyone! We've just released Chrome Dev 103 (103.0.5060.22) for Android. It's now available on Google Play.
You can see a partial list of the changes in the Git log. For details on new features, check out the Chromium blog, and for details on web platform updates, check here.
If you find a new issue, please let us know by filing a bug.
Krishna Govind
Google Chrome
Hi, everyone! We've just released Chrome 102 (102.0.5005.58, 102.0.5005.59) for Android: it'll become available on Google Play over the next few days.
This release includes stability and performance improvements. You can see a full list of the changes in the Git log. If you find a new issue, please let us know by filing a bug.Hi, everyone! We've just released Chrome 102 (102.0.5005.58, 102.0.5005.59) for Android: it'll become available on Google Play over the next few days.
This release includes stability and performance improvements. You can see a full list of the changes in the Git log. If you find a new issue, please let us know by filing a bug.The Chrome team is delighted to announce the promotion of Chrome 102 to the stable channel for Windows (102.0.5005.61/62/63), 102.0.5005.61 for Mac and Linux.Chrome 102 is also promoted to our new extended stable channel for Windows and Mac. This will roll out over the coming days/weeks.
Chrome 102.0.5005.61 contains a number of fixes and improvements -- a list of changes is available in the log. Watch out for upcoming Chrome and Chromium blog posts about new features and big efforts delivered in 102.
Security Fixes and Rewards
Note: Access to bug details and links may be kept restricted until a majority of users are updated with a fix. We will also retain restrictions if the bug exists in a third party library that other projects similarly depend on, but haven’t yet fixed.
This update includes 32 security fixes. Below, we highlight fixes that were contributed by external researchers. Please see the Chrome Security Page for more information.
[$TBD][1324864] Critical CVE-2022-1853: Use after free in Indexed DB. Reported by Anonymous on 2022-05-12
[$10000][1320024] High CVE-2022-1854: Use after free in ANGLE. Reported by SeongHwan Park (SeHwa) on 2022-04-27
[$7500][1228661] High CVE-2022-1855: Use after free in Messaging. Reported by Anonymous on 2021-07-13
[$3000][1323239] High CVE-2022-1856: Use after free in User Education. Reported by Nan Wang(@eternalsakura13) and Guang Gong of 360 Alpha Lab on 2022-05-06
[$2000][1227995] High CVE-2022-1857: Insufficient policy enforcement in File System API. Reported by Daniel Rhea on 2021-07-11
[$1000][1314310] High CVE-2022-1858: Out of bounds read in DevTools. Reported by EllisVlad on 2022-04-07
[$1000][1322744] High CVE-2022-1859: Use after free in Performance Manager. Reported by Guannan Wang (@Keenan7310) of Tencent Security Xuanwu Lab on 2022-05-05
[$TBD][1297209] High CVE-2022-1860: Use after free in UI Foundations. Reported by @ginggilBesel on 2022-02-15
[$TBD][1316846] High CVE-2022-1861: Use after free in Sharing. Reported by Khalil Zhani on 2022-04-16
[$5000][1236325] Medium CVE-2022-1862: Inappropriate implementation in Extensions. Reported by Alesandro Ortiz on 2021-08-04
[$5000][1292870] Medium CVE-2022-1863: Use after free in Tab Groups. Reported by David Erceg on 2022-02-01
[$5000][1320624] Medium CVE-2022-1864: Use after free in WebApp Installs. Reported by Yuntao You (@GraVity0) of Bytedance Wuheng Lab on 2022-04-28
[$3000][1289192] Medium CVE-2022-1865: Use after free in Bookmarks. Reported by Rong Jian of VRI on 2022-01-20
[$3000][1292264] Medium CVE-2022-1866: Use after free in Tablet Mode. Reported by @ginggilBesel on 2022-01-29
[$3000][1315563] Medium CVE-2022-1867: Insufficient validation of untrusted input in Data Transfer. Reported by Michał Bentkowski of Securitum on 2022-04-12
[$TBD][1301203] Medium CVE-2022-1868: Inappropriate implementation in Extensions API. Reported by Alesandro Ortiz on 2022-02-28
[$NA][1309467] Medium CVE-2022-1869: Type Confusion in V8. Reported by Man Yue Mo of GitHub Security Lab on 2022-03-23
[$TBD][1323236] Medium CVE-2022-1870: Use after free in App Service. Reported by Nan Wang(@eternalsakura13) and Guang Gong of 360 Alpha Lab on 2022-05-06
[$7000][1308199] Low CVE-2022-1871: Insufficient policy enforcement in File System API. Reported by Thomas Orlita on 2022-03-21
[$7000][1310461] Low CVE-2022-1872: Insufficient policy enforcement in Extensions API. Reported by ChaobinZhang on 2022-03-26
[$2000][1305394] Low CVE-2022-1873: Insufficient policy enforcement in COOP. Reported by NDevTK on 2022-03-11
[$500][1251588] Low CVE-2022-1874: Insufficient policy enforcement in Safe Browsing. Reported by hjy79425575 on 2021-09-21
[$500][1306443] Low CVE-2022-1875: Inappropriate implementation in PDF. Reported by NDevTK on 2022-03-15
[$TBD][1313600] Low CVE-2022-1876: Heap buffer overflow in DevTools. Reported by @ginggilBesel on 2022-04-06
We would also like to thank all security researchers that worked with us during the development cycle to prevent security bugs from ever reaching the stable channel.
As usual, our ongoing internal security work was responsible for a wide range of fixes:
[1328866] Various fixes from internal audits, fuzzing and other initiatives
Interested in switching release channels? Find out how here. If you find a new issue, please let us know by filing a bug. The community help forum is also a great place to reach out for help or learn about common issues.
The Chrome team is delighted to announce the promotion of Chrome 102 to the stable channel for Windows (102.0.5005.61/62/63), 102.0.5005.61 for Mac and Linux.Chrome 102 is also promoted to our new extended stable channel for Windows and Mac. This will roll out over the coming days/weeks.
Chrome 102.0.5005.61 contains a number of fixes and improvements -- a list of changes is available in the log. Watch out for upcoming Chrome and Chromium blog posts about new features and big efforts delivered in 102.
Security Fixes and Rewards
Note: Access to bug details and links may be kept restricted until a majority of users are updated with a fix. We will also retain restrictions if the bug exists in a third party library that other projects similarly depend on, but haven’t yet fixed.
This update includes 32 security fixes. Below, we highlight fixes that were contributed by external researchers. Please see the Chrome Security Page for more information.
[$TBD][1324864] Critical CVE-2022-1853: Use after free in Indexed DB. Reported by Anonymous on 2022-05-12
[$10000][1320024] High CVE-2022-1854: Use after free in ANGLE. Reported by SeongHwan Park (SeHwa) on 2022-04-27
[$7500][1228661] High CVE-2022-1855: Use after free in Messaging. Reported by Anonymous on 2021-07-13
[$3000][1323239] High CVE-2022-1856: Use after free in User Education. Reported by Nan Wang(@eternalsakura13) and Guang Gong of 360 Alpha Lab on 2022-05-06
[$2000][1227995] High CVE-2022-1857: Insufficient policy enforcement in File System API. Reported by Daniel Rhea on 2021-07-11
[$1000][1314310] High CVE-2022-1858: Out of bounds read in DevTools. Reported by EllisVlad on 2022-04-07
[$1000][1322744] High CVE-2022-1859: Use after free in Performance Manager. Reported by Guannan Wang (@Keenan7310) of Tencent Security Xuanwu Lab on 2022-05-05
[$TBD][1297209] High CVE-2022-1860: Use after free in UI Foundations. Reported by @ginggilBesel on 2022-02-15
[$TBD][1316846] High CVE-2022-1861: Use after free in Sharing. Reported by Khalil Zhani on 2022-04-16
[$5000][1236325] Medium CVE-2022-1862: Inappropriate implementation in Extensions. Reported by Alesandro Ortiz on 2021-08-04
[$5000][1292870] Medium CVE-2022-1863: Use after free in Tab Groups. Reported by David Erceg on 2022-02-01
[$5000][1320624] Medium CVE-2022-1864: Use after free in WebApp Installs. Reported by Yuntao You (@GraVity0) of Bytedance Wuheng Lab on 2022-04-28
[$3000][1289192] Medium CVE-2022-1865: Use after free in Bookmarks. Reported by Rong Jian of VRI on 2022-01-20
[$3000][1292264] Medium CVE-2022-1866: Use after free in Tablet Mode. Reported by @ginggilBesel on 2022-01-29
[$3000][1315563] Medium CVE-2022-1867: Insufficient validation of untrusted input in Data Transfer. Reported by Michał Bentkowski of Securitum on 2022-04-12
[$TBD][1301203] Medium CVE-2022-1868: Inappropriate implementation in Extensions API. Reported by Alesandro Ortiz on 2022-02-28
[$NA][1309467] Medium CVE-2022-1869: Type Confusion in V8. Reported by Man Yue Mo of GitHub Security Lab on 2022-03-23
[$TBD][1323236] Medium CVE-2022-1870: Use after free in App Service. Reported by Nan Wang(@eternalsakura13) and Guang Gong of 360 Alpha Lab on 2022-05-06
[$7000][1308199] Low CVE-2022-1871: Insufficient policy enforcement in File System API. Reported by Thomas Orlita on 2022-03-21
[$7000][1310461] Low CVE-2022-1872: Insufficient policy enforcement in Extensions API. Reported by ChaobinZhang on 2022-03-26
[$2000][1305394] Low CVE-2022-1873: Insufficient policy enforcement in COOP. Reported by NDevTK on 2022-03-11
[$500][1251588] Low CVE-2022-1874: Insufficient policy enforcement in Safe Browsing. Reported by hjy79425575 on 2021-09-21
[$500][1306443] Low CVE-2022-1875: Inappropriate implementation in PDF. Reported by NDevTK on 2022-03-15
[$TBD][1313600] Low CVE-2022-1876: Heap buffer overflow in DevTools. Reported by @ginggilBesel on 2022-04-06
We would also like to thank all security researchers that worked with us during the development cycle to prevent security bugs from ever reaching the stable channel.
As usual, our ongoing internal security work was responsible for a wide range of fixes:
[1328866] Various fixes from internal audits, fuzzing and other initiatives
Interested in switching release channels? Find out how here. If you find a new issue, please let us know by filing a bug. The community help forum is also a great place to reach out for help or learn about common issues.
For years, Waze has partnered with audio services to give you the best, safest and most fun driving experience. Starting today, Apple Music will seamlessly integrate with Waze, so you can keep your eyes on the road while enjoying the ride.
With a direct connection between the apps, you can now access Apple Music content directly from the Waze Audio Player. Enjoy more than 90 million songs, tens of thousands of curated playlists, Apple Music Radio and more while you navigate. We’re thrilled to join forces with Apple Music to bring Apple Music subscribers their tunes while driving with Waze on iPhone.
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.
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| 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.
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| 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.
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| Image classification scaling performance comparing fine-tuned ImageNet top-1 accuracy versus model size. |
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| 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.
Posted by Chris Curtis, Startup Marketing Manager at Google Cloud
We’re excited to announce our annual Google Cloud Startup Summit will be taking place on June 2nd, 2022.
We hope you will join us as we bring together our startup & VC communities. Join us to dive into topics relevant to startups and enjoy sessions such as:
The future of web3
VC AMA: Startup Summit Edition
What’s new for the Google for Startups Cloud Program
Technical leaders & business sessions
Additionally, startups will have an opportunity to join ‘Ask me Anything’ live sessions after the event to interact with Google Cloud startup experts and technical teams to discuss questions that may come up throughout the event.
You can see the full agenda here to get more details on the sessions.
We can’t wait to see you at the Google Cloud Startup Summit. Register to secure your spot today.
Catherine is an Engineering Director for Search and a Tech Site Lead in the Google London office. She’s been managing software engineering teams since the early 90’s and joined Google in 2017 to lead the engineering team working on the Google mobile app.
It’s got to be Hum to Search, without a doubt. If you go into the Google app on your phone and press the microphone button, you can hum a song and it will tell you what the song is. This has helped me quickly identify a tune so many times!
We do have a rigorous testing process, even for fun features like this, to make sure these things are something users can use and actually want. It’s a continuation of the Search premise, to keep answering the questions that niggle at you – but this time via audio.
Probably the fact that it simply keeps getting more helpful, as we combine our understanding of text, voice and images — so you’ll be able to find helpful information about whatever you see, hear and experience, in ways that are most intuitive to you. We’ve developed a helpful new function called multisearch, which means you can search with images and text at the same time. So even if you don’t have the words to describe what you’re looking for, you can get help. For example, you can search for similar products in a different color, or take a picture of wallpaper and ask for it on a blanket instead, or even how to look after the basil plant on your windowsill. We’re envisioning a future where you can search your whole world, any way and anywhere.
We have an incredible team working on Search — people developing the machine learning models, the services, the software on the phone. How well those people communicate determines how well the software fits together, so it’s important people have psychological safety in the job. If they do, it means easy feedback mechanisms, good communication and tight team work.
It’s also down to leadership to make sure teams realize everyone has to succeed for the business to — that it’s really not a competition. When looking for our future Search stars, the whole person matters, not just their skills — so will you put users first, do the right thing, work well with others and create an inclusive environment? Those questions really help determine the right fit.
We’ve got a newish feature called ‘About this result’. When you’re searching for something, you can click an icon that then tells you more about how our systems determined a result might be a good match for your search. You can also find important context about a source or topic, before you visit a website. We’re trying to help people develop information literacy skills — so they can have more context about the sources of their information and understand how Search works. And it means they can be more savvy about what’s going on.
Just the impact. We have billions of users. Lots of people are relying on our information to help them in their daily lives, help them in extreme situations, help them always. It’s really nice to work on something you know people need and want. We are helpful — that’s it really. I rely on it – it’s how I live in my world. I worked in computers long before the internet, and I grew up spending hours in the library just looking things up – Search coming along changed all that. If you’d told me about this as a teenager I would have told you you were crazy!
Hi, everyone! We've just released Chrome 102 (102.0.5005.67) for iOS; it'll become available on App Store in the next few hours.
This release includes stability and performance improvements. You can see a full list of the changes in the Git log. If you find a new issue, please let us know by filing a bug.
Harry Souders
