Summary of Artvlm: Attribute Recognition Through Vision-based Prefix Language Modeling, by William Yicheng Zhu et al.
ArtVLM: Attribute Recognition Through Vision-Based Prefix Language Modeling
by William Yicheng Zhu, Keren Ye, Junjie Ke, Jiahui Yu, Leonidas Guibas, Peyman Milanfar, Feng Yang
First submitted to arxiv on: 7 Aug 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 paper proposes a novel approach to zero-shot visual attribute recognition by leveraging large pre-trained Vision-Language Models (VLMs). The method, called generative retrieval, models object-attribute dependencies as conditional probability graphs and uses VLMs to naturally distill knowledge of image-object-attribute relations. This is in contrast to traditional contrastive retrieval methods that globally align elements of the sentence to the image. Experiments on two visual reasoning datasets, Visual Attribute in the Wild (VAW) and Visual Genome Attribute Ranking (VGARank), demonstrate that generative retrieval outperforms contrastive retrieval. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Visual attribute recognition is a crucial step in many computer vision applications, but it remains a challenge. The paper proposes a new approach called generative retrieval that uses large pre-trained Vision-Language Models to recognize visual attributes. It’s different from traditional methods that just align elements of the sentence to the image. The researchers tested their method on two datasets and found that it worked better than other methods. |
Keywords
» Artificial intelligence » Probability » Zero shot
