Unsupervised Hashing with Semantic Concept Mining
Summary: UHSCM builds a semantic similarity matrix via VLP-based concept mining; concepts are denoised with prompts to capture semantics. A modified contrastive regularizer guided by this matrix boosts unsupervised image retrieval. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Rong-Cheng Tu (Beijing Institute of Technology)
- 2. Xian-Ling Mao (Beijing Institute of Technology)
- 3. Kevin Qinghong Lin (National University of Singapore)
- 4. Chengfei Cai (Zhejiang University)
- 5. Weize Qin (Chinese Academy of Sciences)
- 6. Wei Wei (Huazhong University of Science and Technology)
- 7. Hongfa Wang (Chinese Academy of Sciences)
- 8. Heyan Huang (Beijing Institute of Technology)
BibTeX Citation
@inproceedings{tu_sigmod23,
title = {{Unsupervised Hashing with Semantic Concept Mining}},
author = {Tu, Rong-Cheng and Mao, Xian-Ling and Lin, Kevin Qinghong and Cai, Chengfei and Qin, Weize and Wei, Wei and Wang, Hongfa and Huang, Heyan},
series = {{SIGMOD} '23},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3588683},
url = {https://dl.acm.org/doi/10.1145/3588683},
year = {2023}
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| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 21 | Similarity Search in High Dimensions via Hashing | 1999 | VLDB | 0.00056760516 |
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