期刊
2021 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION, CVPR 2021
卷 -, 期 -, 页码 1407-1417出版社
IEEE COMPUTER SOC
DOI: 10.1109/CVPR46437.2021.00146
关键词
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资金
- National Key Research and Development Program of China [2020AAA0107400]
- National Natural Science Foundation of China [U20A20222]
- Zhejiang Provincial Natural Science Foundation of China [LR19F020004]
- key scientific technological innovation research project by Ministry of Education
The paper introduces a depth-sensitive RGB feature modeling scheme and an automatic architecture search approach for effective feature modeling and multi-modal feature fusion in RGB-D salient object detection (SOD). Extensive experiments demonstrate the effectiveness of the proposed approach against the state-of-the-art methods.
RGB-D salient object detection (SOD) is usually formulated as a problem of classification or regression over two modalities, i.e., RGB and depth. Hence, effective RGB-D feature modeling and multi-modal feature fusion both play a vital role in RGB-D SOD. In this paper, we propose a depth-sensitive RGB feature modeling scheme using the depth-wise geometric prior of salient objects. In principle, the feature modeling scheme is carried out in a depth-sensitive attention module, which leads to the RGB feature enhancement as well as the background distraction reduction by capturing the depth geometry prior. Moreover, to perform effective multi-modal feature fusion, we further present an automatic architecture search approach for RGB-D SOD, which does well in finding out a feasible architecture from our specially designed multi-modal multi-scale search space. Extensive experiments on seven standard benchmarks demonstrate the effectiveness of the proposed approach against the state-of-the-art.
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