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Cross-Modal Implicit Relation Reasoning and Aligning for Text-to-Image Person Retrieval

CVPR 2023 · Vision-language and multimodal › Cross-modal learning · approach: Probabilistic embeddings for cross-modal retrieval · 320 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Add IRR to the CLIP-ViT-B/16 baseline (ablation)CLIP-ViT-B/16 baseline · CUHK-PEDES, ICFG-PEDES, and RSTPReidRank-1: 68.19 -> 71.23; 56.74 -> 60.96; 54.05 -> 57.90Helped
Replace baseline InfoNCE with SDM (ablation)CLIP-ViT-B/16 baseline · CUHK-PEDES, ICFG-PEDES, and RSTPReidRank-1: 68.19 -> 70.42; 56.74 -> 60.45; 54.05 -> 57.20Helped
Replace baseline InfoNCE with CMPM (ablation)CLIP-ViT-B/16 baseline · CUHK-PEDES and ICFG-PEDESRank-1: 68.19 -> 59.31; 56.74 -> 53.83Hurt
Add ID loss to the SDM+IRR configuration (ablation)CLIP-ViT-B/16 baseline with SDM and IRR · CUHK-PEDES, ICFG-PEDES, and RSTPReidRank-1: 72.81 -> 73.38; 63.27 -> 63.46; 59.25 -> 60.20Helped
Replace IRRA's interaction module with Co-attn (ablation)IRRA · CUHK-PEDESinference time: 6.42 -> 24.30Hurt
Replace IRRA's interaction module with Merged attn (ablation)IRRA · CUHK-PEDESinference time: 6.42 -> 19.20Hurt
Use IRRA rather than CFinetext-to-image person retrieval · CUHK-PEDESRank-1: 69.57 -> 73.38Helped
Use IRRA rather than CFinetext-to-image person retrieval · ICFG-PEDESRank-1: +2.63%Helped
Use IRRA rather than CFinetext-to-image person retrieval · RSTPReidRank-1: +9.65%Helped

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