Describe me if you can! Characterized Instance-level Human Parsing
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Abstract (arXiv)
Several computer vision applications such as person search or online fashion rely on human description. The use of instance-level human parsing (HP) is therefore relevant since it localizes semantic attributes and body parts within a person. But how to characterize these attributes? To our knowledge, only some single-HP datasets describe attributes with some color, size and/or pattern characteristics. There is a lack of dataset for multi-HP in the wild with such characteristics. In this article, we propose the dataset CCIHP based on the multi-HP dataset CIHP, with 20 new labels covering these 3 kinds of characteristics. In addition, we propose HPTR, a new bottom-up multi-task method based on transformers as a fast and scalable baseline. It is the fastest method of multi-HP state of the art while having precision comparable to the most precise bottom-up method. We hope this will encourage research for fast and accurate methods of precise human descriptions.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 21 of its 30 references that are in the map. First posted on arXiv 24 Jan 2022.
Builds on
21 of 30 references.
References · 21
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- 2015Deep Human Parsing with Active Template Regressioncited by 51
9 more
- 2017Joint Multi-Person Pose Estimation and Semantic Part Segmentationcited by 41
- 2020Renovating Parsing R-CNN for Accurate Multiple Human Parsingcited by 37
- 2018ModaNet: A Large-Scale Street Fashion Dataset with Polygon Annotationscited by 33
- 2020Fashion Meets Computer Vision: A Surveycited by 30
- 2019Learning Semantic Neural Tree for Human Parsingcited by 29
- 2019Grapy-ML: Graph Pyramid Mutual Learning for Cross-dataset Human Parsingcited by 23
- 2021PandaNet : Anchor-Based Single-Shot Multi-Person 3D Pose Estimationcited by 22
- 2015Clothing Co-Parsing by Joint Image Segmentation and Labelingcited by 20
- 2017Holistic, Instance-Level Human Parsingcited by 19
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Citing papers per year: 2022: 1; 2023: 2.
3 papers.