🚀 Introducing 𝐒𝐚𝐟𝐞𝐖𝐚𝐭𝐜𝐡! 🚀 While generative models 👾🎥 like Sora and Veo 2 have shown us some stunning videos recently, they also make it easier to produce harmful content (sexual🔞, violent🙅‍♂️, deepfakes🧟‍♂️). 🔥 𝐒𝐚𝐟𝐞𝐖𝐚𝐭𝐜𝐡 is here to help 😎: the first MLLM-based video guardrail model designed to follow customized safety policies and provide guardrails with precise explanations in a zero-shot manner. In addition, we also introduce SafeWatch-Bench📊, a 2M high-quality video guardrail dataset covering over 30 unsafe video scenarios from various real-world platforms and SOTA generative models to comprehensively cover all potential risks. 🧐Why SafeWatch? 👉1. Strong policy-following: trained on diverse videos and policy taxonomies, yielding high generalizability to unseen scenarios and subtle policy definitions. 👉2. High Inference Speed: introducing two plug-and-play modules to process policies in parallel and prune irrelevant video tokens, reducing inference costs and eliminating positional bias. 👉3. In-depth explanations: trained on high-quality explanations from SafeWatch-Bench📊 labeled by a rigorous multi-agent consensus pipeline and verified by human experts. We evaluate SafeWatch on a large variety of guardrail tasks: 1️⃣ On both real-world and generative video subsets of SafeWatch-Bench, SafeWatch outperforms SOTAs, including GPT-4o, by 29.2% and 27.2% on average, while requiring much less inference time. 2️⃣ On 5 existing video guardrail benchmarks, SafeWatch achieves 87.1% accuracy, consistently outperforming previous SOTAs. 3️⃣ On 4 new video categories and unseen policy taxonomies, as well as 4 different prompting tasks, SafeWatch maintains high accuracy and outperforms GPT-4o (renowned for its zero-shot generalizability). 🔥🔥 Our project has been released: 👉Paper link: arxiv.org/pdf/2412.06878 👉Project page: safewatch-aiguard.github.io 👉Code (coming soon): github.com/BillChan226/SafeW…

I’ll be at #NeurIPS2024 from now to Sunday. DM here or on Whova to have a chat about (multimodal) large language models privacy, memorisation, training strategies using synthetic data, agents, judges, distribution shift robustness, hallucinations and uncertainty estimation.

Concerned your LLMs 🤖 may regurgitate copyrighted contents ©️ and get you sued? 🩸💸 Fix it with model fusion 🫠 Result of a fantastic collaboration with @JavierAbadM @DonhauserKonst @FannyYangETH 🇨🇭🇬🇧

AI coding assistants (e.g. @cursor_ai, @codeiumdev , @github Copilot) are transforming software development—but how secure are they? Our new blog post reveals which tools stand up to security best practices, which introduce hidden vulnerabilities, and what you can do to safeguard your code. Learn more: virtueai.com/2024/12/09/how-… #ai #coding #copilot #security #safety

Can't wait for our workshop 'Interpretable AI: Past, Present and Future' @NeurIPSConf ! Check out our super interesting program with talks from @NeelNanda5 , @CynthiaRudin , #RichCaruana , @jxzhangjhu and @TongWang! We'll have a panel moderated by the amazing @kamalikac ! Help us spread the word, RTs appreciated!

1/n Happy to share our recent work with @rvolpis @puneetdokania Philip Torr and Grégory Rogez 🚀🤖: Placing Objects in Context via Inpainting for Out-of-distribution Segmentation 🖌️🎨 ->🔍🐏🐏🐺🐏🐏 Paper: arxiv.org/pdf/2402.16392.pdf Code: github.com/naver/poc

In the era of long-context LLMs it is not enough to make models “forget” unsafe knowledge. Adversaries can use this long context to “un-unlearn” the malicious behavior 👿

🔥 Excited to be co-organizing this #ECCV2024 workshop with an outstanding line-up of speakers! 🗣️ 🔎Submit if you got papers with new benchmarks and analyses inspecting Emergent Visual abilities ✔️ or limitations ❌of Foundation Models! 🤖