GRPSNET: Multi-class part parsing based on graph reasoning

Njuod, Alsudays, Wu, Jing ORCID: https://orcid.org/0000-0001-5123-9861, Lai, Yukun ORCID: https://orcid.org/0000-0002-2094-5680 and Ji, Ze ORCID: https://orcid.org/0000-0002-8968-9902 2024. GRPSNET: Multi-class part parsing based on graph reasoning. Presented at: IEEE Conference on Multimedia Expo 2024, Niagara Falls, Canada, 15-19 July 2024. IEEE International Conference on Multimedia and Expo (ICME). IEEE, pp. 1-10. 10.1109/ICME57554.2024.10687831

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Abstract

Multi-class part parsing is a dense prediction task that decomposes objects into semantic components with multi-level abstractions. Despite the importance of this problem, it remains challenging due to the presence of both part-level and class-level ambiguities. In this paper, we propose GRPSNet network which integrates graph reasoning to capture relationships between parts for part segmentation. These captured relationships help to enhance the recognition and localization of parts. We also propose to exploit the relationships of part boundaries to further enhance the accuracy of part segmentation. The experimental results demonstrate the effectiveness of the proposed method and show that it achieves state-of-the-art performance on the benchmark datasets.

Item Type:	Conference or Workshop Item (Paper)
Date Type:	Published Online
Status:	Published
Schools:	Computer Science & Informatics
Publisher:	IEEE
ISBN:	979-8-3503-9015-5
Date of First Compliant Deposit:	15 May 2024
Date of Acceptance:	13 March 2024
Last Modified:	18 Oct 2024 09:34
URI:	https://orca.cardiff.ac.uk/id/eprint/168926

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