Summary of Safewatch: An Efficient Safety-policy Following Video Guardrail Model with Transparent Explanations, by Zhaorun Chen et al.
SafeWatch: An Efficient Safety-Policy Following Video Guardrail Model with Transparent Explanations
by Zhaorun Chen, Francesco Pinto, Minzhou Pan, Bo Li
First submitted to arxiv on: 9 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes an efficient multimodal large language model (MLLM) based video guardrail model called SafeWatch to ensure safety and security across platforms. Unlike current video guardrails, which are either simplistic or impractical, SafeWatch is designed to follow customized safety policies and provide multi-label video guardrail outputs with content-specific explanations in a zero-shot manner. The model uniquely encodes each policy chunk in parallel, eliminating position bias, and incorporates a policy-aware visual token pruning algorithm to reduce computational overhead. The authors also propose SafeWatch-Bench, a large-scale video guardrail benchmark comprising over 2M videos spanning six safety categories, which covers over 30 tasks. SafeWatch outperforms the state-of-the-art by 28.2% on SafeWatch-Bench and 13.6% on benchmarks, while reducing costs by 10%. The model’s explanations are validated by both LLM and human reviews. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper talks about making sure videos online are safe and respectful. Right now, there are some ways to do this, but they’re not very good or practical. The new method, called SafeWatch, is better because it can understand lots of safety rules and explain why it’s keeping certain content from being too bad. It’s like having a special helper that checks videos for you and gives you reasons why something might be inappropriate. The team also created a big test to see how well their new method works, with over 2 million videos and many different types of safety rules. Their new method is better than the old ones at keeping videos safe and respectful. |
Keywords
» Artificial intelligence » Large language model » Pruning » Token » Zero shot
