AIParsing: Anchor-Free Instance-Level Human Parsing

Most state-of-the-art instance-level human parsing models adopt two-stage anchor-based detectors and, therefore, cannot avoid the heuristic anchor box design and the lack of analysis on a pixel level. To address these two issues, we have designed an instance-level human parsing network which is anch...

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Bibliographic Details
Published in:IEEE transactions on image processing 2022, Vol.31, p.5599-5612
Main Authors: Zhang, Sanyi, Cao, Xiaochun, Qi, Guo-Jun, Song, Zhanjie, Zhou, Jie
Format: Article
Language:English
Subjects:
anchor-free
Datasets
Detectors
edge-guided parsing
Head
Image edge detection
Instance-level human parsing
Object detection
Object recognition
Parameter sensitivity
parsing refinement
Pixels
Proposals
Segmentation
Semantics
Task analysis
video human parsing
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Summary:Most state-of-the-art instance-level human parsing models adopt two-stage anchor-based detectors and, therefore, cannot avoid the heuristic anchor box design and the lack of analysis on a pixel level. To address these two issues, we have designed an instance-level human parsing network which is anchor-free and solvable on a pixel level. It consists of two simple sub-networks: an anchor-free detection head for bounding box predictions and an edge-guided parsing head for human segmentation. The anchor-free detector head inherits the pixel-like merits and effectively avoids the sensitivity of hyper-parameters as proved in object detection applications. By introducing the part-aware boundary clue, the edge-guided parsing head is capable to distinguish adjacent human parts from among each other up to 58 parts in a single human instance, even overlapping instances. Meanwhile, a refinement head integrating box-level score and part-level parsing quality is exploited to improve the quality of the parsing results. Experiments on two multiple human parsing datasets ( i.e. , CIHP and LV-MHP-v2.0) and one video instance-level human parsing dataset ( i.e. , VIP) show that our method achieves the best global-level and instance-level performance over state-of-the-art one-stage top-down alternatives.
ISSN:1057-7149
1941-0042
DOI:10.1109/TIP.2022.3192989