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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CDGNet: Class Distribution Guided Network for Human Parsing
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Part-aware Panoptic Segmentation
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Looking at Outfit to Parse ClothingPreprint
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Deep Learning Approaches for Fashion Knowledge Extraction From Social Media: A Review
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Image-to-Video Person Re-Identification With Temporally Memorized Similarity Learning
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You can try without visiting: a comprehensive survey on virtually try-on outfits
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Adaptive Temporal Encoding Network for Video Instance-level Human Parsing
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Self-citationTask-Aware Attention Model for Clothing Attribute Prediction
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HumanBench: Towards General Human-Centric Perception with Projector Assisted Pretraining
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Trusted Guidance Pyramid Network for Human Parsing
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Fine-Grained Multi-human Parsing
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Self-citationProgressive Cognitive Human Parsing
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Fashion Meets Computer Vision: A SurveyPreprint
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Panoptic-PartFormer: Learning a Unified Model for Panoptic Part Segmentation
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Recognizing Focal Liver Lesions in CEUS With Dynamically Trained Latent Structured Models
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Self-citationDifferentiable Multi-Granularity Human Parsing
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Human Pose Estimation from Depth Images via Inference Embedded Multi-task Learning
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Deep Portrait Image Completion and Extrapolation
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A Dynamic Mode Decomposition Based Edge Detection Method for Art Images
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Toward Fashion Intelligence in the Big Data Era: State-of-the-Art and Future Prospects
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Deep Cross-Domain Fashion Recommendation
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DDet: Dual-Path Dynamic Enhancement Network for Real-World Image Super-Resolution
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Self-citationUP-CNN: Un-pooling augmented convolutional neural network
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Self-citationGlobal-residual and Local-boundary Refinement Networks for Rectifying Scene Parsing Predictions
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Human Parsing With Pyramidical Gather-Excite Context
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Quality-Aware Network for Human Parsing
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Deep Learning Technique for Human Parsing: A Survey and Outlook
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- 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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