Summary of Cala: Complementary Association Learning For Augmenting Composed Image Retrieval, by Xintong Jiang et al.
CaLa: Complementary Association Learning for Augmenting Composed Image Retrieval
by Xintong Jiang, Yaxiong Wang, Mengjian Li, Yujiao Wu, Bingwen Hu, Xueming Qian
First submitted to arxiv on: 29 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR)
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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 paper presents a novel approach to Composed Image Retrieval (CIR) by exploring additional associations within the query-image-text triplet beyond primary matching. The authors introduce two new relations: text-bridged image alignment and complementary text reasoning. A hinge-based cross-attention mechanism is proposed for incorporating these relations into network learning, followed by a twin attention-based compositor to integrate perspectives. The framework, CaLa, leverages these insights and is evaluated on CIRR and FashionIQ benchmarks with multiple backbones, demonstrating superiority in CIR tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about finding pictures based on what you write about them. Right now, computers just match the words to the picture. But this paper shows that there’s more to it than that. It finds new ways to connect the words and pictures together. The computer uses two special techniques: one helps find similar pictures by using the written description as a bridge, and another looks at how the words describe the pictures. This combination helps the computer learn better from the query-image-text triplet. The result is a system called CaLa that does a great job of finding pictures based on descriptions. |
Keywords
» Artificial intelligence » Alignment » Attention » Cross attention
