Summary of Dual Relation Alignment For Composed Image Retrieval, by Xintong Jiang et al.
Dual Relation Alignment for Composed Image Retrieval
by Xintong Jiang, Yaxiong Wang, Yujiao Wu, Meng Wang, Xueming Qian
First submitted to arxiv on: 5 Sep 2023
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 framework for composed image retrieval, dubbed dual relation alignment, integrates both explicit and implicit relations to fully exploit the correlations among triplets. By fusing reference images and target images, a vision compositor generates representations that serve as counterparts for semantic alignment with complementary texts. This novel approach is evaluated on two popular datasets, CIRR and FashionIQ, through extensive experiments, demonstrating substantial enhancements in composed image retrieval performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you’re trying to find an exact picture of your favorite animal by searching through a bunch of images and descriptions. This task is called composed image retrieval, where you use a reference image and some text about it to find the perfect match. People have been working on making this process better, but there’s still room for improvement. Researchers found that there are actually two types of connections between the reference image, the target image, and the description: one is obvious (the reference image is related to the target image), and another is more subtle (the description can be understood by looking at how the reference image relates to the target image). To take advantage of both connections, they created a new way of searching called dual relation alignment. This method combines both types of connections to find better matches between images and descriptions. |
Keywords
» Artificial intelligence » Alignment
