Research map / Label-efficient and robust learning / Recognition and long-tail
Exploiting Unlabelled Photos for Stronger Fine-Grained SBIR
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Add sketch and photo intra-modal triplet losses to cross-modal triplet training (ablation) | Ours-Strong · QMUL-Shoe-V2 | Acc.@1 (%): 43.28 -> 45.39 | Helped |
| Add EMA to the cross-modal and intra-modal triplet objectives (ablation) | Ours-Strong · QMUL-Shoe-V2 | Acc.@1 (%): 45.39 -> 46.50 | Helped |
| Add labelled-photo distillation to unlabelled-photo distillation (ablation) | Ours-Full · QMUL-Shoe-V2 | Acc.@1 (%): 46.50 -> 47.21 | Helped |
| Add sketch-to-photo contextual alignment distillation (ablation) | Ours-Full · QMUL-Shoe-V2 | Acc.@1 (%): 47.21 -> 48.35 | Helped |
| Add unlabelled-photo distillation to Ours-Strong | Ours-Full · QMUL-Shoe-V2 | Acc.@1 (%): 44.18 -> 48.35 | Helped |
| Add unlabelled-photo distillation to Ours-Strong | Ours-Full · QMUL-Chair-V2 | Acc.@1 (%): 71.22 -> 74.68 | Helped |
| Use a dedicated distillation token rather than the discriminative feature for distillation (ablation) | Ours-Full · QMUL-Shoe-V2 | Acc.@1 (%): 44.71 -> 48.35 | Helped |
| Process the distillation token at every PVT level via residual connections rather than only at the last stage (ablation) | Ours-Full · QMUL-Shoe-V2 | Acc.@1 (%): 45.31 -> 48.35 | Helped |
| Use structural photo augmentation for intra-modal triplet training instead of colour distortion (ablation) | Ours-Strong · QMUL-Shoe-V2 | Acc.@1 (%): 46.14 | Helped |
| Use structural photo augmentation for intra-modal triplet training instead of partial blurring (ablation) | Ours-Strong · QMUL-Shoe-V2 | Acc.@1 (%): 47.32 | Helped |
| Use structural photo augmentation for intra-modal triplet training instead of random sharpness shift (ablation) | Ours-Strong · QMUL-Shoe-V2 | Acc.@1 (%): 47.54 | Helped |
| Use photo intra-modal triplet loss rather than contrastive loss (ablation) | Ours-Strong · QMUL-Shoe-V2 | Acc.@1 (%): 45.68 | Helped |
Closest papers by meaning
- ARNet: Self-Supervised FG-SBIR with Unified Sample Feature Alignment and Multi-Scale Token Recycling (AAAI 2025 · similarity 0.92)
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