Today we have released the code and a demo iOS application for FastVLM - our extremely efficient and fast vision language model which runs on your device using MLX! You can check out the code and the app here: github.com/apple/ml-fas...

Join us! Registration is required. simons.berkeley.edu/events/move-...

If you're looking for research scientist roles in Europe, check out Marco's post! The Paris team is fantastic, and does diverse idea-driven and impactful research. In addition, MLR is highly collaborative across timezones, so you'd have a chance to work with many others too.

Excited about vision-language models? 🚀 Check out our latest work on FastVLM, a new family of efficient vision-language models that balances the tradeoff between high-resolution image understanding and latency without compromising accuracy! arxiv.org/abs/2412.13303

🤔Image-to-3D, monocular depth estimation, camera pose estimation, …, can we achieve all of this with just ONE model easily? 🚀Our answer is Yes -- Excited to introduce our latest work: World-consistent Video Diffusion (WVD) with Explicit 3D Modeling! arxiv.org/abs/2412.01821

𝗗𝗼𝗲𝘀 𝗮𝘂𝘁𝗼𝗿𝗲𝗴𝗿𝗲𝘀𝘀𝗶𝘃𝗲 𝗽𝗿𝗲-𝘁𝗿𝗮𝗶𝗻𝗶𝗻𝗴 𝘄𝗼𝗿𝗸 𝗳𝗼𝗿 𝘃𝗶𝘀𝗶𝗼𝗻? 🤔 Delighted to share AIMv2, a family of strong, scalable, and open vision encoders that excel at multimodal understanding, recognition, and grounding 🧵 paper: arxiv.org/abs/2411.14402 code: github.com/apple/ml-aim HF: huggingface.co/collections/...

Looking for an alternative to RAG for personalization? With PLUM, a pipeline for teaching LLMs to remember prior user conversations, we aim to enable your future personalization research! Joint work with @maartjeterhoeve.bsky.social, Katherine Metcalf and Yizhe Zhang from my internship at Apple. 🧵