Research map / Label-efficient and robust learning / Noisy labels
Mutual Quantization for Cross-Modal Search With Noisy Labels
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use CMMQ instead of the strongest reported baseline | Cross-modal hashing with noisy labels · MIRFlickr-25K, NUS-WIDE, and MS-COCO; I2T and T2I | mAP: 4.78%/2.7% in average mAP | Helped |
| Add the mutual quantization loss (λ = 0.7) (ablation) | CMMQ · MIRFlickr-25K | mAP | Helped |
| Vary λ for the mutual quantization loss from 0 to 1 (ablation) | CMMQ · MIRFlickr-25K, 32-bit codes | mAP | Mixed |
| Select small-loss examples for training | CMMQ · MIRFlickr-25K | mAP | Helped |
| Use small-loss selection and the proposed method rather than the BCE loss | Cross-modal hashing · MIRFlickr-25K test data | mAP | Helped |
Closest papers by meaning
- Semantic-Consistent Bidirectional Contrastive Hashing for Noisy Multi-Label Cross-Modal Retrieval (AAAI 2026 · similarity 0.92)
- Meta-Guided Sample Reweighting for Robust Cross-Modal Hashing Retrieval with Noisy Labels (AAAI 2026 · similarity 0.92)
- Dual Self-Paced Cross-Modal Hashing (AAAI 2024 · similarity 0.91)
- Robust Self-Paced Hashing for Cross-Modal Retrieval with Noisy Labels (AAAI 2025 · similarity 0.91)
- Exploiting Descriptive Completeness Prior for Cross Modal Hashing with Incomplete Labels (NeurIPS 2024 · similarity 0.91)
- Lightweight Contrastive Distilled Hashing for Online Cross-modal Retrieval (AAAI 2025 · similarity 0.90)
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