Olivier Hénaff @olivierhenaff
Staff Research Scientist @GoogleDeepMind, interested in active, multimodal, and memory-augmented learning. Formerly @NYU_CNS and @Polytechnique olivierhenaff.com London, UK Joined May 2019-
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Latest work where we present OpenEQA, a modern embodied Q&A benchmark which tests multiple capabilities such as spatial reasoning, object recognition and world knowledge on which SOTA VLMS like GPT4V/Claude/Gemini fail. A new challenge for embodied AI! To be presented @CVPR.
Latest work where we present OpenEQA, a modern embodied Q&A benchmark which tests multiple capabilities such as spatial reasoning, object recognition and world knowledge on which SOTA VLMS like GPT4V/Claude/Gemini fail. A new challenge for embodied AI! To be presented @CVPR.
This was such a good time, and a nice opportunity to reflect on why I think AI research can be used to deepen our understanding of the human experience. Thanks so much @misssaxbys for inviting me and @JoshuaWohle for hosting!
This was such a good time, and a nice opportunity to reflect on why I think AI research can be used to deepen our understanding of the human experience. Thanks so much @misssaxbys for inviting me and @JoshuaWohle for hosting!
Super excited to chat with @misssaxbys about brains, machines, vision, memory, and art and what they can all learn from each other tomorrow at the @MindstoneHQ AI meetup tomorrow. Do stop by if you're in London! app.mindstone.com/mindspace/mind…
Encoding videos spatiotemporally as a long seq using ViT demonstratably improves performance, but frame-level representations remain the go-to due to memory and computational constraints... Well, this is no longer the case with a non-parametric memory requiring minimal training!
Encoding videos spatiotemporally as a long seq using ViT demonstratably improves performance, but frame-level representations remain the go-to due to memory and computational constraints... Well, this is no longer the case with a non-parametric memory requiring minimal training!
Google Deepmind presents Memory Consolidation Enables Long-Context Video Understanding paper page: huggingface.co/papers/2402.05… Most transformer-based video encoders are limited to short temporal contexts due to their quadratic complexity. While various attempts have been made to…
DeepMind presents MC-ViT Sets a new SotA in long-context video understanding, outperforming methods that benefit from orders of magnitude more parameters arxiv.org/abs/2402.05861
Super proud of our new work on memory consolidation for long video understanding! Great collaboration with amazing colleagues, resulting in a paper with 3 female joint first authors 🎉
Improvements from using longer context is unsurprising, but we found a way to do it ✨in style✨ with x10 memory savings with greater perf gain. A super fun and rewarding experience working with Ivana Balazevic (@ibalazevic) and Olivier Henaff (@olivierhenaff) and many more!
Improvements from using longer context is unsurprising, but we found a way to do it ✨in style✨ with x10 memory savings with greater perf gain. A super fun and rewarding experience working with Ivana Balazevic (@ibalazevic) and Olivier Henaff (@olivierhenaff) and many more!
🤔 Weight averaging is great, but can you merge arbitrary trained models? Our #ICLR2024 spotlight shows that you can - and should! For example, we find that weak models have unique knowledge missed by bigger and better ones, and careful integration can make them even stronger!
Bring your AI knowledge right up to date as @graphcoreai's Charlie Blake shares the latest 'Papers of the Month': Great teachers, beyond Chinchilla, improving diffusion training and Olympiad geometry.distillationpod.buzzsprout.com/2277524/144277…
Summer Research Internships in Computational Neuroscience @FlatironCCN Graduate students and advanced undergraduates: Spend a summer doing research at the Flatiron Institute in NYC! Application deadline 2 Feb 2024: apply.interfolio.com/137388
Welcome back Nikhil!!
A bit belated, but happy to share that I defended my PhD thesis! Thanks to my advisor @EeroSimoncelli for all the support along the way! This week I'm excited to start a new chapter as a Research Scientist @GoogleDeepMind building models of vision with @olivierhenaff and others!
AK @_akhaliq
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712K Followers 719 Following Professor at NYU. Chief AI Scientist at Meta. Researcher in AI, Machine Learning, Robotics, etc. ACM Turing Award Laureate.Google DeepMind @GoogleDeepMind
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229K Followers 3K Following @NVIDIA Sr. Research Manager & Lead of Embodied AI (GEAR Lab). Creating foundation models for Humanoid Robots & Gaming. @Stanford Ph.D. @OpenAI's first intern.Oriol Vinyals @OriolVinyalsML
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112K Followers 35 Following New Orleans, Dec 10-16, 23. https://t.co/ga8aOw615g Tweets to this account are not monitored. Please send feedback to [email protected].Thomas Kipf @tkipf
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Latest work where we present OpenEQA, a modern embodied Q&A benchmark which tests multiple capabilities such as spatial reasoning, object recognition and world knowledge on which SOTA VLMS like GPT4V/Claude/Gemini fail. A new challenge for embodied AI! To be presented @CVPR.
Today we’re releasing OpenEQA — the Open-Vocabulary Embodied Question Answering Benchmark. It measures an AI agent’s understanding of physical environments by probing it with open vocabulary questions like “Where did I leave my badge?” More details ➡️ go.fb.me/7vq6hm…
@arimorcos @NandoDF That’s a weird take. Don’t you think griffin is a cool new model, and the data is the usual stuff?
No "Zero-Shot" Without Exponential Data: Pretraining Concept Frequency Determines Multimodal Model Performance Web-crawled pretraining datasets underlie the impressive "zero-shot" evaluation performance of multimodal models, such as CLIP for classification/retrieval and Stable-
@giffmana @srush_nlp @CVPR If you want real-time performance, particularly for detection/localization, ConvNets are still way more practical/efficient than ViTs.
@karpathy I think you are rediscovering the concept of “vacation”.
I’ll give a talk about UViM (and similar methods X-Decoder, UnifiedIO, etc), RL-tuning in vision and maybe PaLI. Very relevant to recent LLM+vision I’ll do my best to make the general idea as accessible as possible rather than saying “here’s our method, it’s sota, look, tables”
Announcing 📢 a special ZurichCV Meetup #2! Lucas Beyer @giffmana from @GoogleDeepMind will be speaking about structured computer vision in the age of LLMs on April 16th at 17:00. Come check it out! 🚀 zurich-nlp.ch/event/zurichcv…
Straight to perf. No detours. I’m not even finishing my current dota game.
@giffmana doing tge heavy lifting for gemini's pr team
@giffmana +1 for this! I always point people to the FixRes paper. Also surprising that we don't consider resolution more given the capabilities of human peripheral vs foveal vision... But ofc the right thing to do is to move to resolution adaptive architectures like NaViT and FlexiViT.
@pmddomingos Suddenly, many of your tweets look like jokes, Pedro.
I’m thrilled to unveil @Waabi_ai latest research, Copilot4D: the first foundation model that explicitly reasons in both 3D space and time and leverages LiDAR. Big step forward in bringing generative AI to the physical world. Learn more here: waabi.ai/introducing-co…
Amazing fireside chat at the @MindstoneHQ London AI meetup as always with @misssaxbys and @olivierhenaff :)
Discussing memory, art + what AI can teach us about human perception and the mind in a church ⛪️ was certainly a career highlight. A huge thank you to @olivierhenaff for sharing your insights and @JoshuaWohle for having us
Discussing memory, art + what AI can teach us about human perception and the mind in a church ⛪️ was certainly a career highlight. A huge thank you to @olivierhenaff for sharing your insights and @JoshuaWohle for having us
Today, I am very proud share what we have been working on for the last 14 months. ✨ Introducing Aya -- a new state-of-art for massively multilingual models. 🔥🎉
Today, we’re launching Aya, a new open-source, massively multilingual LLM & dataset to help support under-represented languages. Aya outperforms existing open-source models and covers 101 different languages – more than double covered by previous models. cohere.com/research/aya
@neilhoulsby @YugeTen @ibalazevic @olivierhenaff It’s already happening :)
@YugeTen @ibalazevic @olivierhenaff Very nice, bravo Jimmy and collaborators👏 Only thing, I am a little disappointed in those parameter counts, you gotta pump those numbers up!
@olivierhenaff @ibalazevic @YugeTen @pinelopip3 @Rahma_Chaa @skandakoppula Hi, really cool work! Some questions: - the evaluation datasets consist of ~158 frames (Diving48), 3 min (EgoSchema), 44 seconds (Next-QA), etc. How is that "far into the past"? Isn't this still very short-term? - any plan to open-source the work which would 🚀🚀 the impact?
Implementing this on my GitHub
Humans and animals reason about events spanning days, weeks, and years, yet current CV systems live largely in the present. Introducing Memory-Consolidated ViT, whose context extends far into the past and sets a new SOTA in long-video understanding with a 10x smaller model