Papers › Parsing R-CNN for Instance-Level Human Analysis

Parsing R-CNN for Instance-Level Human Analysis

30 Nov 2018CVPR 2019 6arXiv:1811.12596archive 2025-07-28

Lu Yang, Qing Song, Zhihui Wang, Ming Jiang

Instance-level human analysis is common in real-life scenarios and has multiple manifestations, such as human part segmentation, dense pose estimation, human-object interactions, etc. Models need to distinguish different human instances in the image panel and learn rich features to represent the details of each instance. In this paper, we present an end-to-end pipeline for solving the instance-level human analysis, named Parsing R-CNN. It processes a set of human instances simultaneously through comprehensive considering the characteristics of region-based approach and the appearance of a human, thus allowing representing the details of instances. Parsing R-CNN is very flexible and efficient, which is applicable to many issues in human instance analysis. Our approach outperforms all state-of-the-art methods on CIHP (Crowd Instance-level Human Parsing), MHP v2.0 (Multi-Human Parsing) and DensePose-COCO datasets. Based on the proposed Parsing R-CNN, we reach the 1st place in the COCO 2018 Challenge DensePose Estimation task. Code and models are public available.

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fast_hist soeaver/Parsing-R-CNN/rcnn/modeling/parsing_rcnn/loss.py official repository ran · fixture could not drive it MIT (permissive) · d86fb168246b16a4 · report
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Tasks

Human ParsingHuman Part SegmentationMulti-Human ParsingPose Estimation

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Human Part Segmentation CIHP Parsing R-CNN + ResNext101 Mean IoU 61.1 #5 of 6 Archive leaderboard report
Human Part Segmentation MHP v2.0 Parsing R-CNN + ResNext101 Mean IoU 41.8 #1 of 1 Archive leaderboard report
Pose Estimation DensePose-COCO Parsing R-CNN + ResNext101 AP 61.6 #2 of 4 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

1x1 ConvolutionAverage PoolingBatch NormalizationConvolutionGlobal Average PoolingGrouped ConvolutionKaiming InitializationReLUResNeXtResNeXt BlockResidual Connection

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