Multi-human Parsing with a Graph-based Generative Adversarial Model
| Autor: | Jiashi Feng, Yunchao Wei, Guodong Guo, Jianshu Li, Congyan Lang, Shuicheng Yan, Jian Zhao, Yidong Li, Terence Sim |
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| Rok vydání: | 2021 |
| Předmět: |
Theoretical computer science
Parsing Computer Networks and Communications Computer science Graph based 02 engineering and technology 010501 environmental sciences computer.software_genre 01 natural sciences Task (project management) Image (mathematics) Adversarial system 0803 Computer Software 0805 Distributed Computing 0806 Information Systems Hardware and Architecture 0202 electrical engineering electronic engineering information engineering Graph (abstract data type) Artificial Intelligence & Image Processing 020201 artificial intelligence & image processing computer Generative grammar 0105 earth and related environmental sciences |
| Zdroj: | ACM Transactions on Multimedia Computing, Communications, and Applications. 17:1-21 |
| ISSN: | 1551-6865 1551-6857 |
| DOI: | 10.1145/3418217 |
| Popis: | Human parsing is an important task in human-centric image understanding in computer vision and multimedia systems. However, most existing works on human parsing mainly tackle the single-person scenario, which deviates from real-world applications where multiple persons are present simultaneously with interaction and occlusion. To address such a challenging multi-human parsing problem, we introduce a novel multi-human parsing model named MH-Parser, which uses a graph-based generative adversarial model to address the challenges of close-person interaction and occlusion in multi-human parsing. To validate the effectiveness of the new model, we collect a new dataset named Multi-Human Parsing (MHP), which contains multiple persons with intensive person interaction and entanglement. Experiments on the new MHP dataset and existing datasets demonstrate that the proposed method is effective in addressing the multi-human parsing problem compared with existing solutions in the literature. |
| Databáze: | OpenAIRE |
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