Summary of Cluster-aware Similarity Diffusion For Instance Retrieval, by Jifei Luo et al.
Cluster-Aware Similarity Diffusion for Instance Retrieval
by Jifei Luo, Hantao Yao, Changsheng Xu
First submitted to arxiv on: 4 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
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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 Cluster-Aware Similarity (CAS) diffusion method for instance retrieval is proposed to overcome limitations of existing techniques that construct affinity graphs based on pairwise instances. The primary concept of CAS involves conducting similarity propagation within local clusters to reduce influence from other manifolds, thus improving accuracy. To achieve a symmetrical and smooth similarity matrix, the Bidirectional Similarity Diffusion strategy introduces an inverse constraint term to the optimization objective. Additionally, a Neighbor-guided Similarity Smoothing approach ensures consistency among local neighbors. The proposed CAS is evaluated in instance retrieval and object re-identification tasks, demonstrating its effectiveness. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to find similar things (like pictures or objects) is developed. This method helps by focusing on groups of similar things rather than just looking at each individual thing. It does this by spreading the similarity score from one group to another, which reduces the influence of noisy or incorrect information. The team behind this project also added a way to make sure the similarity scores are consistent between different parts of the same group. This new method is tested and shown to work well in finding similar pictures and objects. |
Keywords
» Artificial intelligence » Diffusion » Optimization
