Summary of Multi-path Exploration and Feedback Adjustment For Text-to-image Person Retrieval, by Bin Kang et al.
Multi-path Exploration and Feedback Adjustment for Text-to-Image Person Retrieval
by Bin Kang, Bin Chen, Junjie Wang, Yong Xu
First submitted to arxiv on: 26 Oct 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 A novel approach for text-based person retrieval is presented, addressing the limitations of existing methods relying on vision-language pre-trained (VLP) models. The proposed Multi-Pathway Exploration, Feedback, and Adjustment framework, MeFa, leverages intrinsic feedback to improve person-text associations. This framework comprises three pathways: intra-modal reasoning, cross-modal refinement, and discriminative clue correction. These pathways refine local information through global alignment biases and self-feedback regulation, leading to more precise person retrieval performance. The paper demonstrates superior results on three public benchmarks without requiring additional data or complex structures. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you’re searching for someone based on a description of what they look like, but you can’t see them. Existing methods use special models that help match text with images, but these models have some limitations. A new approach, called MeFa, tries to overcome these limitations by giving more importance to the information within each text and image separately. This helps to find the right person even if they don’t perfectly match the description. The results show that this method works better than existing methods without needing more data or complicated structures. |
Keywords
» Artificial intelligence » Alignment
