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Non-local Aggregation for RGB-D Semantic Segmentation

Zhang, G; Xue, J-H; Xie, P; Yang, F; Wang, G; (2021) Non-local Aggregation for RGB-D Semantic Segmentation. IEEE Signal Processing Letters 10.1109/lsp.2021.3066071. Green open access

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Abstract

Exploiting both RGB (2D appearance) and Depth (3D geometry) information can improve the performance of semantic segmentation. However, due to the inherent difference between the RGB and Depth information, it remains a challenging problem in how to integrate RGB-D features effectively. In this letter, to address this issue, we propose a Non-local Aggregation Network (NANet), with a well-designed Multi-modality Non-local Aggregation Module (MNAM), to better exploit the non-local context of RGB-D features at multi-stage. Compared with most existing RGB-D semantic segmentation schemes, which only exploit local RGB-D features, the MNAM enables the aggregation of non-local RGB-D information along both spatial and channel dimensions. The proposed NANet achieves comparable performances with state-of-the-art methods on popular RGB-D benchmarks, NYUDv2 and SUN-RGBD.

Type: Article
Title: Non-local Aggregation for RGB-D Semantic Segmentation
Open access status: An open access version is available from UCL Discovery
DOI: 10.1109/lsp.2021.3066071
Publisher version: http://dx.doi.org/10.1109/lsp.2021.3066071
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science
URI: https://discovery.ucl.ac.uk/id/eprint/10124455
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