Research map / Vision-language and multimodal / Cross-modal learning
Deep Cross-Modal Hashing
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Jointly learn modality features and hash codes end to end instead of using hand-crafted features | DCMH · MIRFLICKR-25K and NUS-WIDE, both retrieval directions | MAP | Helped |
| Jointly learn modality features and hash codes end to end rather than train hashing methods on fixed CNN-F features | DCMH · MIRFLICKR-25K, both retrieval directions | MAP: 0.7090 -> 0.7150; 0.7127 -> 0.7545 | Helped |
| Use end-to-end DCMH rather than kernel-based SePH | Cross-modal retrieval with CNN-F features · NUS-WIDE, image query to text database | MAP: 0.6538 vs 0.6249 | Hurt |
| Use end-to-end DCMH rather than kernel-based SePH | Cross-modal retrieval with CNN-F features · NUS-WIDE, text query to image database | MAP: 0.6600 -> 0.6791 | Helped |
| Use DCMH rather than STMH | Cross-modal hash lookup · MIRFLICKR-25K, both retrieval directions, Hamming radii 0, 1, and 2 | F-measure | Helped |
| Use end-to-end DCMH rather than CNN-F features with SePH-linear | Cross-modal retrieval · MIRFLICKR-25K and NUS-WIDE, all reported cases | MAP | Helped |
Closest papers by meaning
- Dual Self-Paced Cross-Modal Hashing (AAAI 2024 · similarity 0.91)
- Lightweight Contrastive Distilled Hashing for Online Cross-modal Retrieval (AAAI 2025 · similarity 0.91)
- Vision-guided Text Mining for Unsupervised Cross-modal Hashing with Community Similarity Quantization (AAAI 2025 · similarity 0.90)
- An End-To-End Graph Attention Network Hashing for Cross-Modal Retrieval (NeurIPS 2024 · similarity 0.89)
- Robust Self-Paced Hashing for Cross-Modal Retrieval with Noisy Labels (AAAI 2025 · similarity 0.89)
- Asymmetric Cross-Modal Hashing Based on Formal Concept Analysis (AAAI 2025 · similarity 0.89)
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