Papers › Parsing R-CNN for Instance-Level Human Analysis
Parsing R-CNN for Instance-Level Human Analysis
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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Code Syntology ran Syntology
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| 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
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