Summary of Visco: Benchmarking Fine-grained Critique and Correction Towards Self-improvement in Visual Reasoning, by Xueqing Wu et al.
VISCO: Benchmarking Fine-Grained Critique and Correction Towards Self-Improvement in Visual Reasoning
by Xueqing Wu, Yuheng Ding, Bingxuan Li, Pan Lu, Da Yin, Kai-Wei Chang, Nanyun Peng
First submitted to arxiv on: 3 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 proposed VISCO benchmark assesses the fine-grained critique and correction capabilities of large vision-language models (LVLMs). Unlike existing work that critiques entire reasoning, VISCO requires LVLMs to evaluate each step in a chain-of-thought and provide natural language explanations. The evaluation of 24 LVLMs demonstrates that human-written critiques enhance performance after correction, but model-generated critiques are less helpful or even detrimental. This highlights the critique as a crucial bottleneck. Three common patterns in critique failures were identified: failure to critique visual perception, reluctance to “say no”, and exaggerated assumption of error propagation. To address these issues, the LookBack strategy revisits the image to verify each piece of information, improving critique and correction performance by up to 13.5%. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large vision-language models (LVLMs) need to learn how to correct their mistakes. But have scientists really tested how good they are at doing this? The VISCO benchmark is the first to look closely at how well LVLMs can fix their mistakes and explain why they’re right or wrong. It turns out that humans are much better at helping models improve than the models themselves are. This is important because it shows where we need to focus our efforts to make models smarter. |
