Summary of Psa-vlm: Enhancing Vision-language Model Safety Through Progressive Concept-bottleneck-driven Alignment, by Zhendong Liu et al.
PSA-VLM: Enhancing Vision-Language Model Safety through Progressive Concept-Bottleneck-Driven Alignment
by Zhendong Liu, Yuanbi Nie, Yingshui Tan, Jiaheng Liu, Xiangyu Yue, Qiushi Cui, Chongjun Wang, Xiaoyong Zhu, Bo Zheng
First submitted to arxiv on: 18 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 This research proposes a novel approach to enhancing the safety alignment of Vision Language Models (VLMs) by introducing a progressive concept-based alignment strategy, PSA-VLM. By integrating safety modules as concept bottlenecks into the visual modality, PSA-VLM improves defenses against harmful images while maintaining general performance. The method involves two-stage training: an initial low-computational-cost stage that brings significant performance improvement and a fine-tuning stage that further enhances safety performance. PSA-VLM achieves state-of-the-art results on popular VLM safety benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you have a special kind of computer program called a Vision Language Model (VLM). This program can understand both words and pictures, which sounds cool! However, someone might try to trick the program by sending it bad or harmful images. The researchers in this paper want to make sure VLMs are safe and won’t do something bad if they see a bad picture. They came up with a new way to make VLMs safer by adding special filters that check what kind of image is being sent. This makes the program more predictable and harder for someone to trick it. |
Keywords
» Artificial intelligence » Alignment » Fine tuning » Language model
