Summary of Rebalanced Vision-language Retrieval Considering Structure-aware Distillation, by Yang Yang et al.
Rebalanced Vision-Language Retrieval Considering Structure-Aware Distillation
by Yang Yang, Wenjuan Xi, Luping Zhou, Jinhui Tang
First submitted to arxiv on: 14 Dec 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 The proposed method addresses the issue of modal imbalance in vision-language retrieval by learning structure-preserved matching representations. The authors demonstrate that traditional cross-modal matching is sub-optimal when imbalanced modalities exist and propose a novel multi-granularity cross-modal matching approach that incorporates structure-aware distillation alongside the cross-modal matching loss. This approach enhances both single-modal and cross-modal retrieval performance on various datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you’re trying to find similar images based on descriptions, but some types of images have more information than others. This is called modal imbalance, and it makes it hard for computers to find good matches. The authors of this paper show that when there’s imbalance, traditional methods don’t work well. They propose a new way to match images and text together by learning from both the image and text sides. Their method does better than previous methods at finding similar images and texts. |
Keywords
» Artificial intelligence » Distillation
