The DOI matches its Crossref record; 34 of the 35 references it cites were found in a registry or an index.
Human Parsing with Contextualized Convolutional Neural Network
Abstract
In this work, we address the human parsing task with a novel Contextualized Convolutional Neural Network (Co-CNN) architecture, which well integrates the cross-layer context, global image-level context, within-super-pixel context and cross-super-pixel neighborhood context into a unified network. Given an input human image, Co-CNN produces the pixel-wise categorization in an end-to-end way. First, the cross-layer context is captured by our basic local-to-global-to-local structure, which hierarchically combines the global semantic structure and the local fine details within the cross-layers. Second, the global image-level label prediction is used as an auxiliary objective in the intermediate layer of the Co-CNN, and its outputs are further used for guiding the feature learning in subsequent convolutional layers to leverage the global image-level context. Finally, to further utilize the local super-pixel contexts, the within-super-pixel smoothing and cross-super-pixel neighbourhood voting are formulated as natural sub-components of the Co-CNN to achieve the local label consistency in both training and testing process. Comprehensive evaluations on two public datasets well demonstrate the significant superiority of our Co-CNN architecture over other state-of-the-arts for human parsing. In particular, the F-1 score on the large dataset [15] reaches 76.95% by Co-CNN, significantly higher than 62.81% and 64.38% by the state-of-the-art algorithms, M-CNN [21] and ATR [15], respectively.
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Metadata
- Authors
- Liang, X., Xu, C., Shen, X., Yang, J., Liu, S., Tang, J., Lin, L., & Yan, S.
- Affiliations
- Sun Yat-sen University (CN)
- National University of Singapore (SG)
- Adobe Systems (United States) (US)
- Snap (United States) (US)
- Chinese Academy of Sciences (CN)
- Nanjing University of Science and Technology (CN)
- Venue
- 2015 IEEE International Conference on Computer Vision (ICCV)
- Publisher
- IEEE
- Published
- 2015-12
- Pages
- 1386-1394
- OpenAlex
- W2204578866
- Cited by
- 245
- References
- 35
- Citations by year
- 2026: 6 · 2025: 8 · 2024: 22 · 2023: 16 · 2022: 28 · 2021: 28 · 2020: 26 · 2019: 38 · 2018: 28 · 2017: 31 · 2016: 13 · 2015: 1
- FWCI
- 9.98
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE/CVF International Conference on Computer Vision (ICCV) (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Human Pose and Action Recognition
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Keywords
- human parsing, hierarchical feature learning
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