Meta presents Image World Model Learning and Leveraging World Models in Visual Representation Learning arxiv.org/abs/2403.00504

Meta presents Learning and Leveraging World Models in Visual Representation Learning Joint-Embedding Predictive Architecture (JEPA) has emerged as a promising self-supervised approach that learns by leveraging a world model. While previously limited to predicting missing parts

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Introducing Meta Llama 3: the most capable openly available LLM to date. Today we’re releasing 8B & 70B models that deliver on new capabilities such as improved reasoning and set a new state-of-the-art for models of their sizes. Today's release includes the first two Llama 3

More technical details on the new Meta Llama 3 models announced today. 🦙🧵

ViT need registers got an outstanding paper award! Many thanks to the comittee for the honor

New research from FAIR: Better & Faster Large Language Models via Multi-token Prediction Research paper ➡️ go.fb.me/wty7gj We show that replacing next token prediction tasks with multiple token prediction can result in substantially better code generation performance

Revisiting Feature Prediction for Learning Visual Representations from Video ➡️ arxiv.org/abs/2404.08471

What happens if we make language models predict several tokens ahead instead of only the next one? In this paper, we show that multi-token prediction boosts language model training efficiency. 🧵 1/11 Paper: arxiv.org/abs/2404.19737 Joint work with Fabian Gloeckle

📝 New from FAIR: An Introduction to Vision-Language Modeling. Vision-language models (VLMs) are an area of research that holds a lot of potential to change our interactions with technology, however there are many challenges in building these types of models. Together with a set

Are sota image generative models effective world models? Consistency-diversity-realism Pareto fronts show they're not (yet): - No model dominates others as a world model - Improvements in quality and consistency have come at the expense of diversity 🔗 arxiv.org/abs/2406.10429

Yann LeCun from AI at Meta greets the public at the exclusive Open Source AI Day event at STATION F at panel with adriennejan from Scaleway, Thomas Wolf from Hugging Face and Patrick Pérez from kyutai Join us as lnkd.in/gwphNpV2 We will continue by great...

Starting today, open source is leading the way. Introducing Llama 3.1: Our most capable models yet. Today we’re releasing a collection of new Llama 3.1 models including our long awaited 405B. These models deliver improved reasoning capabilities, a larger 128K token context

Introducing Meta Segment Anything Model 2 (SAM 2) — the first unified model for real-time, promptable object segmentation in images & videos. SAM 2 is available today under Apache 2.0 so that anyone can use it to build their own experiences Details ➡️ go.fb.me/p749s5

Job alert 🚨 My team AI at Meta is looking for a PhD intern to join us in 2025 in Paris. We are working on self-supervised learning from video, world modelling and JEPA ! Apply here or reach out directly: metacareers.com/jobs/168411027…

Want strong SSL, but not the complexity of DINOv2? CAPI: Cluster and Predict Latents Patches for Improved Masked Image Modeling.

The last paper of my PhD is finally out ! Introducing "Intuitive physics understanding emerges from self-supervised pretraining on natural videos" We show that without any prior, V-JEPA --a self-supervised video model-- develops an understanding of intuitive physics !

Does your LLM truly comprehend the complexity of the code it generates? 🥰   Introducing our new non-saturated (for at least the coming week? 😉) benchmark:   ✨BigO(Bench)✨ - Can LLMs Generate Code with Controlled Time and Space Complexity?   Check out the details below !👇

Our vision is for AI that uses world models to adapt in new and dynamic environments and efficiently learn new skills. We’re sharing V-JEPA 2, a new world model with state-of-the-art performance in visual understanding and prediction. V-JEPA 2 is a 1.2 billion-parameter model,