Towards Unified Human Parsing and Pose Estimation

By Jian Dong, Qiang Chen, Xiaohui Shen, Jianchao Yang, Shuicheng Yan. 2014

110 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Human-Computer Interaction

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Luc Van Gool
2. Xiaogang Wang
3. Alan L. Yuille
4. Yi Yang
5. Dacheng Tao
6. Ming-Hsuan Yang
7. Liang Lin
8. Wanli Ouyang

In Human-Computer Interaction

1. Zhe L. Lin
2. Brian L. Price
3. Hao Zhang
4. Fei Han
5. William A. Hoff
6. Brian Reily
7. Wanli Ouyang
8. Shuicheng Yan

Citations in the corpus

• Space-Time Representation of People Based on 3D Skeletal Data: A Review (2016, methodology): “Methods based on a single image: Many algorithms were proposed to construct human skeletal model using a single color or intensity image acquired from a monocular camera [65], [68], [69], [89].”
• Look into Person: Self-Supervised Structure-Sensitive Learning and a New Benchmark for Human Parsing (2017, background): “Some previous works [8, 29] explored human pose information to guide human parsing by generating “pose-guided” part segment proposals.”
• Semantic part segmentation using compositional model combining shape and appearance (2014, background): “But there are only a few pieces of works on semantic part segmentation, such as human parsing [4, 10, 9, 29] and car parsing [26, 11, 23].”
• Progressive One-shot Human Parsing (2020, unknown): “One direction is modeling the parsing task together with the keypoint detection task (Xia et al. 2017; Nie et al. 2018; Huang, Gong, and Tao 2017; Fang et al. 2018; Dong et al. 2014; Zhang et al. 2020).”
• Joint Object and Part Segmentation Using Deep Learned Potentials (2015, background): “It should be noted that such localization is still very coarse with much looser bounding boxes than the ones needed in previous parsing methods [11, 39].”
• Zoom Better to See Clearer: Human and Object Parsing with Hierarchical Auto-Zoom Net (2015, background): “The study of human part parsing has been largely restricted to constrained environments, where a human instance in an image is well localized and has a relatively simple pose like standing or walking [3,36,9,34,8,21,33].”
• Attention-Aware Compositional Network for Person Re-identification (2018, background): “Human parsing [17, 10, 47, 26, 15] is related to our work in that parsing results can accurately localize body part.”
• Cross-Domain Image Retrieval with a Dual Attribute-Aware Ranking Network (2015, background): “Clothing parsing methods, which produce semantic labels for each pixel in the input image, have received significant attention in the past few years [45, 9].”
• Deep domain adaptation for describing people based on fine-grained clothing attributes (2015, background): “(b) the Parsing dataset [8] which consists of 8000 street fashion images.”
• Look into Person: Joint Body Parsing & Pose Estimation Network and a New Benchmark (2018, background): “Conversely, some pose-guided human parsing methods [33], [34] also sufficiently utilized the peculiarity and relationships of these two correlated tasks.”