Research map / Vision-language and multimodal / Cross-modal learning
Online Cross-Modal Hashing for Web Image Retrieval
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| use online OCMH with shared latent codes instead of batch-retrained CVH, IMH and CMFH | OCMH · NUS-WIDE | MAP (top 50) | Helped |
| use cross-modal OCMH instead of OSH on concatenated image-text features | OCMH · NUS-WIDE | MAP (top 50) | Helped |
| use OCMH instead of CVH | OCMH · MIR Flickr | MAP (top 50) | No clear effect |
| use OCMH instead of other methods | OCMH · MIR Flickr | MAP (top 50) | Helped |
| update only SLC and transfer matrix online instead of retraining and recomputing all codes (CVH) | OCMH · MIR Flickr | learning time | Helped |
| update hash codes via SLC instead of recomputing all database codes (OSH) | OCMH · NUS-WIDE | learning time per round | Helped |
| use OCMH on larger database (NUS-WIDE vs MIR Flickr) | OCMH · NUS-WIDE vs MIR Flickr | MAP gain over baselines | Helped |
| add more rounds of streaming data | OCMH · NUS-WIDE | MAP | Helped |
| train IMH on 10% random subset of database due to cost | IMH · NUS-WIDE and MIR Flickr | learning time: 10% | Mixed |
Closest papers by meaning
- Lightweight Contrastive Distilled Hashing for Online Cross-modal Retrieval (AAAI 2025 · similarity 0.90)
- Dual Self-Paced Cross-Modal Hashing (AAAI 2024 · similarity 0.89)
- An End-To-End Graph Attention Network Hashing for Cross-Modal Retrieval (NeurIPS 2024 · similarity 0.89)
- Vision-guided Text Mining for Unsupervised Cross-modal Hashing with Community Similarity Quantization (AAAI 2025 · similarity 0.88)
- Asymmetric Cross-Modal Hashing Based on Formal Concept Analysis (AAAI 2025 · similarity 0.88)
- Robust Self-Paced Hashing for Cross-Modal Retrieval with Noisy Labels (AAAI 2025 · similarity 0.87)
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