乐胖代购免代理版

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Li, Deep RGB-D saliency detection with depth-sensitive attention and automatic multi-modal fusion, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2021, pp. 1407\u20131417.","DOI":"10.1109\/CVPR46437.2021.00146"},{"key":"10.1016\/j.knosys.2024.111597_b70","doi-asserted-by":"crossref","first-page":"4253","DOI":"10.1109\/TMM.2022.3172852","article-title":"Depth-induced gap-reducing network for RGB-D salient object detection: An interaction, guidance and refinement approach","volume":"25","author":"Cheng","year":"2023","journal-title":"IEEE Trans. Multimed."},{"key":"10.1016\/j.knosys.2024.111597_b71","doi-asserted-by":"crossref","unstructured":"M. Lee, C. Park, S. Cho, S. Lee, Spsn: Superpixel prototype sampling network for rgb-d salient object detection, in: Proceedings of the European Conference on Computer Vision, 2022, pp. 630\u2013647.","DOI":"10.1007\/978-3-031-19818-2_36"},{"key":"10.1016\/j.knosys.2024.111597_b72","series-title":"2022 International Conference on 3D Vision","first-page":"403","article-title":"Robust rgb-d fusion for saliency detection","author":"Wu","year":"2022"},{"issue":"5","key":"10.1016\/j.knosys.2024.111597_b73","doi-asserted-by":"crossref","first-page":"2949","DOI":"10.1109\/TCSVT.2021.3099120","article-title":"CGFNet: Cross-guided fusion network for RGB-T salient object detection","volume":"32","author":"Wang","year":"2021","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"issue":"5","key":"10.1016\/j.knosys.2024.111597_b74","doi-asserted-by":"crossref","first-page":"3111","DOI":"10.1109\/TCSVT.2021.3102268","article-title":"Efficient context-guided stacked refinement network for RGB-T salient object detection","volume":"32","author":"Huo","year":"2021","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"10.1016\/j.knosys.2024.111597_b75","first-page":"1","article-title":"Modal complementary fusion network for RGB-T salient object detection","author":"Ma","year":"2022","journal-title":"Appl. Intell."},{"key":"10.1016\/j.knosys.2024.111597_b76","doi-asserted-by":"crossref","first-page":"3752","DOI":"10.1109\/TIP.2022.3176540","article-title":"Weakly alignment-free RGBT salient object detection with deep correlation network","volume":"31","author":"Tu","year":"2022","journal-title":"IEEE Trans. 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