Wild tech you have to try: groq.com They are serving Mixtral at nearly 500 tok/s. Answers are pretty much instantaneous. Opens up new use-cases, and completely changes the UX possibilities of existing ones.

Announcing Stable Diffusion 3, our most capable text-to-image model, utilizing a diffusion transformer architecture for greatly improved performance in multi-subject prompts, image quality, and spelling abilities. Today, we are opening the waitlist for early preview. This phase

Today, we're announcing Claude 3, our next generation of AI models. The three state-of-the-art models—Claude 3 Opus, Claude 3 Sonnet, and Claude 3 Haiku—set new industry benchmarks across reasoning, math, coding, multilingual understanding, and vision.

Call for Papers: #INRV2024 Workshop on Implicit Neural Representation for Vision @ #CVPR2024! Topics: Compression, Representation using INR’s for images, audio, video & more! Ddl: 3/31. Submit now! #CVPR2025 Website: inrv.github.io Submission Link: shorturl.at/vzBR8

Thank you King Kohli 👑 for all the memories. End of an era.

That's a wrap! Happy to share that I have defended my thesis. Thankful for the insightful questions and feedback from my committee members Abhinav Shrivastava,Tianyi Zhou, David Jacobs, Prof. Espy-Wilson, and Prof. Andrew Zisserman.

Excited to announce that I have joined AI at Meta as a Research Scientist where I will be working on model optimization. Also I will be at ECCV to present my work and am excited to meet and learn from everyone. Reach out if you are attending and would like to chat. Ciao 🇮🇹

🚨VideoLLM from Meta!🚨 LongVU: Spatiotemporal Adaptive Compression for Long Video-Language Understanding 📝Paper: huggingface.co/papers/2410.17… 🧑🏻‍💻Code: github.com/Vision-CAIR/Lo… 🚀Project (Demo): vision-cair.github.io/LongVU We propose LongVU, a video LLM with a spatiotemporal adaptive

We are happy to release our LiFT code and pretrained models! 📢 Code: github.com/saksham-s/lift Project Page: cs.umd.edu/~sakshams/LiFT Here are some super spooky super resolved feature visualizations to make the season scarier 🎃 Coauthors: Matthew Walmer Kamal Gupta Abhinav Shrivastava

Checkout LARP, our work on creating a video tokenizer which is trained with an autoregressive generative prior. Code and models are open sourced!

Checkout Efficient Track Anything from our team. 2x faster than SAM2 on A100 > 10 FPS on iPhone 15 Pro Max Paper: arxiv.org/pdf/2411.18933 demo: yformer.github.io/efficient-trac…

[1/n] 𝗘𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝘁 𝗧𝗿𝗮𝗰𝗸 𝗔𝗻𝘆𝘁𝗵𝗶𝗻𝗴 from Meta: interactive video segmentation and tracking on an iPhone!

📢 Excited to announce LARP has been accepted to #ICLR2025 ! 🇸🇬 Code and models are publicly available. Project page: hywang66.github.io/larp/index.html

Today is the start of a new era of natively multimodal AI innovation. Today, we’re introducing the first Llama 4 models: Llama 4 Scout and Llama 4 Maverick — our most advanced models yet and the best in their class for multimodality. Llama 4 Scout • 17B-active-parameter model

Drop by our oral presentation and poster session to chat and learn about our video tokenizer with learned autoregressive prior. #ICLR2025