Research map / Label-efficient and robust learning / Recognition and long-tail
Discrete Image Hashing Using Large Weakly Annotated Photo Collections
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| add discrete optimization of binary codes (no relaxation) to collaborative supervision (ablation) | collaborative-filtering hashing (CH variant) · NUSWIDE and CCV retrieval | Precision@K | Helped |
| use collaborative supervision treating unobserved labels as missing instead of negative | discrete hashing (vs SDH) · NUSWIDE and CCV retrieval | Precision@K | Helped |
| initialize U and B with SVD solution of relaxed factorization (ablation) | discrete weakly-supervised hashing · 0.1M sub-sample, 32 bits, objective convergence | objective value | Helped |
| vary lambda1 (supervised vs quantization loss trade-off) (ablation) | discrete weakly-supervised hashing · NUSWIDE and CCV | mean precision | No clear effect |
| increase code length with supervised hashing | supervised hashing methods · NUSWIDE and CCV | Precision@K vs L2 baseline | Helped |
Closest papers by meaning
- Weakly Supervised Deep Image Hashing Through Tag Embeddings (CVPR 2019 · similarity 0.91)
- Deep Supervised Discrete Hashing (NeurIPS 2017 · similarity 0.91)
- Deep Supervised Hashing for Fast Image Retrieval (CVPR 2016 · similarity 0.91)
- Discretely Coding Semantic Rank Orders for Supervised Image Hashing (CVPR 2017 · similarity 0.90)
- Generalized Debiased Semi-Supervised Hashing for Large-Scale Image Retrieval (AAAI 2025 · similarity 0.88)
- Learning Attribute-Aware Hash Codes for Fine-Grained Image Retrieval via Query Optimization (ICML 2025 · similarity 0.88)
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