Summary of Scaling Inference-time Search with Vision Value Model For Improved Visual Comprehension, by Xiyao Wang et al.
Scaling Inference-Time Search with Vision Value Model for Improved Visual Comprehension
by Xiyao Wang, Zhengyuan Yang, Linjie Li, Hongjin Lu, Yuancheng Xu, Chung-Ching Lin, Kevin Lin, Furong Huang, Lijuan Wang
First submitted to arxiv on: 4 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Computation and Language (cs.CL); 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 A novel vision-language model, VisVM, is introduced to enhance response quality by scaling inference-time computation. This approach guides vision-language models (VLMs) to generate responses with better visual comprehension by evaluating sentence quality and anticipating the quality of subsequent sentences. VisVM steers VLMs away from generating hallucinations or insufficient details, producing higher quality responses. Experimental results demonstrate that VisVM-guided search enhances VLMs’ ability to generate descriptive captions with richer visual details and fewer hallucinations compared to greedy decoding and other methods. The code is available at this https URL. The proposed VisVM model can be used for developing self-improving VLMs, which have the potential to improve performance across a wide range of multimodal benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new computer program called VisVM helps language models make better decisions when generating text descriptions. It does this by considering not just how good the current sentence is, but also how well it will lead to future sentences being good too. This makes the model less likely to come up with silly or irrelevant information and more likely to create detailed and accurate descriptions. The program was tested on a variety of tasks and showed significant improvements over other methods. |
Keywords
* Artificial intelligence * Inference * Language model
