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Learning Attribute and Class-Specific Representation Duet for Fine-Grained Fashion Analysis

CVPR 2023 · Label-efficient and robust learning › Recognition and long-tail · approach: Contrastive representation theory · 9 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
learn attribute- and class-specific representations jointly rather than MODC's inference-stage clusteringfine-grained fashion retrieval · DeepFashionMAP@all: 70.42%Helped
learn attribute- and class-specific representations jointly rather than MODC's inference-stage clusteringfine-grained fashion retrieval · FashionAIMAP@allHelped
learn attribute- and class-specific representations jointly rather than MODC's inference-stage clusteringfine-grained fashion retrieval · DARNMAP@all: 16.35%Helped
add attribute-conditional attention to class-conditional attention (ablation)M3-Net · DeepFashionMAP@all: 20.92 -> 21.78Helped
add class-conditional attention to attribute-conditional attention (ablation)M3-Net · DeepFashionMAP@all: 19.03 -> 21.78Helped
add attribute-conditional attention to class-conditional attention (ablation)M3-Net · FashionAIMAP@all: 72.88 -> 75.04Helped
add class-conditional attention to attribute-conditional attention (ablation)M3-Net · FashionAIMAP@all: 73.93 -> 75.04Helped
add class-conditional attention to attribute-conditional attention (ablation)M3-Net · DARNMAP@all: 71.06 -> 72.42Helped
add attribute-conditional attention to class-conditional attention (ablation)M3-Net · DARNMAP@all: 72.79 -> 72.42No clear effect

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