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Research map / Detection and segmentation / CNN features

Joint Multi-Person Pose Estimation and Semantic Part Segmentation

CVPR 2017 · Detection and segmentation › CNN features · approach: Context modeling for semantic segmentation · 242 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
add part segment consistency features to pose assembly (ablation)Our Model · PASCAL-Person-PartmAP: 38.7 -> 39.2Helped
add part segment consistency features to pose assembly (ablation)Our Model · PASCAL-Person-PartADK (%): 41.9 -> 40.7Helped
add estimated pose features to part segmentation (ablation)Our model (ResNet-101) · PASCAL-Person-PartmIOU (%): 62.66 -> 64.39Helped
add estimated pose features to part segmentation (ablation)Our model (VGG-16) · PASCAL-Person-PartmIOU (%): 56.50 -> 58.06Helped
add estimated pose features to part segmentation (ablation)Our model (ResNet-101) · PASCAL-Person-Part, Size XSmIOU (%): 40.4 -> 53.4Helped
use ResNet-101 instead of VGG-16 for Part FCN (ablation)Our model with pose features · PASCAL-Person-PartmIOU (%): 58.06 -> 64.39Helped
use one pose configuration per detection box instead of multiple (ablation)FCRF pose inference · PASCAL-Person-Partpose estimation resultsNo clear effect
perform refinement once rather than iteratively (ablation)Joint pose estimation and part segmentation framework · PASCAL-Person-Partoverall performanceNo clear effect

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