Summary of Distribution-consistency-guided Multi-modal Hashing, by Jin-yu Liu et al.
Distribution-Consistency-Guided Multi-modal Hashing
by Jin-Yu Liu, Xian-Ling Mao, Tian-Yi Che, Rong-Cheng Tu
First submitted to arxiv on: 15 Dec 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 proposed Distribution-Consistency-Guided Multi-modal Hashing (DCGMH) method aims to filter and reconstruct noisy labels in multi-modal hashing, enhancing retrieval performance. By discovering a significant distribution consistency pattern, the method filters out noisy and clean labels separately, correcting high-confidence ones while treating low-confidence ones as unlabeled for unsupervised learning. This approach demonstrates superior performance compared to state-of-the-art baselines on three widely used datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers developed a new method called DCGMH that helps with multi-modal hashing by fixing problems caused by noisy labels. Noisy labels are when people make mistakes when labeling things, which can cause the computer’s results to be bad. The DCGMH method finds a pattern in how noisy labels look and uses it to fix them. This makes the computer’s results better. They tested their method on three big datasets and found that it worked better than other methods. |
Keywords
» Artificial intelligence » Multi modal » Unsupervised
