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Exploiting Unlabelled Photos for Stronger Fine-Grained SBIR

CVPR 2023 · Label-efficient and robust learning › Recognition and long-tail · approach: Correspondence and causal representation learning · 29 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Add sketch and photo intra-modal triplet losses to cross-modal triplet training (ablation)Ours-Strong · QMUL-Shoe-V2Acc.@1 (%): 43.28 -> 45.39Helped
Add EMA to the cross-modal and intra-modal triplet objectives (ablation)Ours-Strong · QMUL-Shoe-V2Acc.@1 (%): 45.39 -> 46.50Helped
Add labelled-photo distillation to unlabelled-photo distillation (ablation)Ours-Full · QMUL-Shoe-V2Acc.@1 (%): 46.50 -> 47.21Helped
Add sketch-to-photo contextual alignment distillation (ablation)Ours-Full · QMUL-Shoe-V2Acc.@1 (%): 47.21 -> 48.35Helped
Add unlabelled-photo distillation to Ours-StrongOurs-Full · QMUL-Shoe-V2Acc.@1 (%): 44.18 -> 48.35Helped
Add unlabelled-photo distillation to Ours-StrongOurs-Full · QMUL-Chair-V2Acc.@1 (%): 71.22 -> 74.68Helped
Use a dedicated distillation token rather than the discriminative feature for distillation (ablation)Ours-Full · QMUL-Shoe-V2Acc.@1 (%): 44.71 -> 48.35Helped
Process the distillation token at every PVT level via residual connections rather than only at the last stage (ablation)Ours-Full · QMUL-Shoe-V2Acc.@1 (%): 45.31 -> 48.35Helped
Use structural photo augmentation for intra-modal triplet training instead of colour distortion (ablation)Ours-Strong · QMUL-Shoe-V2Acc.@1 (%): 46.14Helped
Use structural photo augmentation for intra-modal triplet training instead of partial blurring (ablation)Ours-Strong · QMUL-Shoe-V2Acc.@1 (%): 47.32Helped
Use structural photo augmentation for intra-modal triplet training instead of random sharpness shift (ablation)Ours-Strong · QMUL-Shoe-V2Acc.@1 (%): 47.54Helped
Use photo intra-modal triplet loss rather than contrastive loss (ablation)Ours-Strong · QMUL-Shoe-V2Acc.@1 (%): 45.68Helped

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