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Research map / Vision-language and multimodal / Cross-modal learning

Robust Multi-Modality Person Re-identification

AAAI 2021 · Vision-language and multimodal › Cross-modal learning · approach: Multi-modal object re-identification · 61 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Progressively fuse RGB, NI, and TI instead of directly concatenating their featuresResNet50 Re-ID on RGBNT201 · RGBNT201mAP: 24.77 -> 31.76Helped
Progressively fuse RGB, NI, and TI instead of directly concatenating their featuresResNet50 Re-ID on RGBNT201 · RGBNT201rank-1: 45.85 -> 54.59Helped
Add spatial attention (SA) (ablation)RGBNT201 three-modality baseline · RGBNT201mAP: 24.77 -> 26.13Helped
Add spatial attention (SA) (ablation)RGBNT201 three-modality baseline · RGBNT201rank-1: 45.85 -> 47.32Helped
Add cross-modality fusion (CMF) (ablation)RGBNT201 three-modality baseline · RGBNT201mAP: 24.77 -> 25.78Helped
Add cross-modality fusion (CMF) (ablation)RGBNT201 three-modality baseline · RGBNT201rank-1: 45.85 -> 49.09Helped
Add both cross-modality fusion and spatial attention (ablation)RGBNT201 three-modality baseline · RGBNT201mAP: 24.77 -> 31.76Helped
Add both cross-modality fusion and spatial attention (ablation)RGBNT201 three-modality baseline · RGBNT201rank-1: 45.85 -> 54.59Helped
Add NI to RGB using PFNet fusionResNet50 Re-ID · RGBNT201mAP: 13.10 -> 17.95Helped
Add TI to RGB using PFNet fusionResNet50 Re-ID · RGBNT201mAP: 13.10 -> 30.23Helped
Use all three modalities with PFNet instead of RGB-onlyResNet50 Re-ID · RGBNT201rank-1: 25.16 -> 54.59Helped
Use progressive fusion rather than feature concatenation for RGB and NIResNet50 Re-ID · RGBNT201mAP: 14.69 -> 17.95Helped

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