Tree Hierarchical CNNs for Object Parsing

Zhang, Xiaomei; Chen, Yingying; Zhu, Bingke; Wang, Jinqiao; Tang, Ming; Lu, Hanqing

doi:10.1109/icip.2018.8451301

Cited by 3 publications

References 8 publications

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Part-Aware Context Network for Human Parsing

Zhang

Chen

Zhu

et al. 2020

2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

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As a fine-grained segmentation task, human parsing is still faced with two challenges: inter-part indistinction and intra-part inconsistency, due to the ambiguous definitions and confusing relationships between similar human parts. To tackle these two problems, this paper proposes a novel Affinity-aware Compression and Expansion Network (ACENet), which mainly consists of two modules: Local Compression Module (LCM) and Global Expansion Module (GEM). Specifically, LCM compresses parts-correlation information through structural skeleton points, obtained from an extra skeleton branch. It can decrease the inter-part interference, and strengthen structural relationships between ambiguous parts. Furthermore, GEM expands semantic information of each part into a complete piece by incorporating the spatial affinity with boundary guidance, which can effectively enhance the semantic consistency of intra-part as well. ACENet achieves new state-of-the-art performance on the challenging LIP and Pascal-Person-Part datasets. In particular, 58.1% mean IoU is achieved on the LIP benchmark.

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