{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,12]],"date-time":"2026-03-12T15:36:53Z","timestamp":1773329813276,"version":"3.50.1"},"reference-count":71,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2023,5,3]],"date-time":"2023-05-03T00:00:00Z","timestamp":1683072000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["U20A20157"],"award-info":[{"award-number":["U20A20157"]}]},{"name":"National Natural Science Foundation of China","award":["62001063"],"award-info":[{"award-number":["62001063"]}]},{"name":"National Natural Science Foundation of China","award":["U2133211"],"award-info":[{"award-number":["U2133211"]}]},{"name":"National Natural Science Foundation of China","award":["2020M673135"],"award-info":[{"award-number":["2020M673135"]}]},{"name":"China Postdoctoral Science Foundation","award":["U20A20157"],"award-info":[{"award-number":["U20A20157"]}]},{"name":"China Postdoctoral Science Foundation","award":["62001063"],"award-info":[{"award-number":["62001063"]}]},{"name":"China Postdoctoral Science Foundation","award":["U2133211"],"award-info":[{"award-number":["U2133211"]}]},{"name":"China Postdoctoral Science Foundation","award":["2020M673135"],"award-info":[{"award-number":["2020M673135"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Multi-modal feature fusion and effectively exploiting high-level semantic information are critical in salient object detection (SOD). However, the depth maps complementing RGB image fusion strategies cannot supply effective semantic information when the object is not salient in the depth maps. Furthermore, most existing (UNet-based) methods cannot fully exploit high-level abstract features to guide low-level features in a coarse-to-fine fashion. In this paper, we propose a compensated attention feature fusion and hierarchical multiplication decoder network (CAF-HMNet) for RGB-D SOD. Specifically, we first propose a compensated attention feature fusion module to fuse multi-modal features based on the complementarity between depth and RGB features. Then, we propose a hierarchical multiplication decoder to refine the multi-level features from top down. Additionally, a contour-aware module is applied to enhance object contour. Experimental results show that our model achieves satisfactory performance on five challenging SOD datasets, including NJU2K, NLPR, STERE, DES, and SIP, which verifies the effectiveness of the proposed CAF-HMNet.<\/jats:p>","DOI":"10.3390\/rs15092393","type":"journal-article","created":{"date-parts":[[2023,5,4]],"date-time":"2023-05-04T01:33:36Z","timestamp":1683164016000},"page":"2393","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Compensated Attention Feature Fusion and Hierarchical Multiplication Decoder Network for RGB-D Salient Object Detection"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7852-9172","authenticated-orcid":false,"given":"Zhihong","family":"Zeng","sequence":"first","affiliation":[{"name":"School of Microelectronics and Communication Engineering, Chongqing University, Chongqing 400044, China"}]},{"given":"Haijun","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Microelectronics and Communication Engineering, Chongqing University, Chongqing 400044, China"}]},{"given":"Fenglei","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Microelectronics and Communication Engineering, Chongqing University, Chongqing 400044, China"}]},{"given":"Xiaoheng","family":"Tan","sequence":"additional","affiliation":[{"name":"School of Microelectronics and Communication Engineering, Chongqing University, Chongqing 400044, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,5,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1007\/s41095-019-0149-9","article-title":"Salient object detection: A survey","volume":"5","author":"Borji","year":"2019","journal-title":"Comput. Vis. Media"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"265","DOI":"10.1109\/TIP.2018.2867198","article-title":"Learning rotation-invariant and fisher discriminative convolutional neural networks for object detection","volume":"28","author":"Cheng","year":"2018","journal-title":"IEEE Trans. Image Process."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"359","DOI":"10.1109\/TMM.2015.2389616","article-title":"Database saliency for fast image retrieval","volume":"17","author":"Gao","year":"2015","journal-title":"IEEE Trans. Multimed."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"4555","DOI":"10.1109\/TIP.2016.2592701","article-title":"Visual tracking via coarse and fine structural local sparse appearance models","volume":"25","author":"Jia","year":"2016","journal-title":"IEEE Trans. Image Process."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Chen, F., Liu, H., Zeng, Z., Zhou, X., and Tan, X. (2022). BES-Net: Boundary enhancing semantic context network for high-resolution image semantic segmentation. Remote Sens., 14.","DOI":"10.3390\/rs14071638"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1305","DOI":"10.1109\/TIP.2020.3042084","article-title":"Dense attention fluid network for salient object detection in optical remote sensing images","volume":"30","author":"Zhang","year":"2020","journal-title":"IEEE Trans. Image Process."},{"key":"ref_7","first-page":"4793","article-title":"Deep hough transform for semantic line detection","volume":"44","author":"Zhao","year":"2021","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Zhu, C., Li, G., Wang, W., and Wang, R. (2017, January 22\u201329). An innovative salient object detection using center-dark channel prior. Proceedings of the IEEE International Conference on Computer Vision Workshops, Venice, Italy.","DOI":"10.1109\/ICCVW.2017.178"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"569","DOI":"10.1109\/TPAMI.2014.2345401","article-title":"Global contrast based salient region detection","volume":"37","author":"Cheng","year":"2014","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015, January 5\u20139). U-net: Convolutional networks for biomedical image segmentation. Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, Munich, Germany.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_11","unstructured":"Zhao, J.X., Liu, J.J., Fan, D.P., Cao, Y., Yang, J., and Cheng, M.M. (November, January 27). EGNet: Edge guidance network for salient object detection. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Seoul, Repulic of Korea."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"638","DOI":"10.1109\/JBHI.2022.3140853","article-title":"Margin preserving self-paced contrastive learning towards domain adaptation for medical image segmentation","volume":"26","author":"Liu","year":"2022","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"102617","DOI":"10.1016\/j.media.2022.102617","article-title":"Source-free domain adaptation for image segmentation","volume":"82","author":"Bateson","year":"2022","journal-title":"Med Image Anal."},{"key":"ref_14","unstructured":"Stan, S., and Rostami, M. (2021). Domain Adaptation for the Segmentation of Confidential Medical Images. arXiv."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"4976","DOI":"10.1109\/JBHI.2022.3162118","article-title":"A novel 3D unsupervised domain adaptation framework for cross-modality medical image segmentation","volume":"26","author":"Yao","year":"2022","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Zhang, H., Fromont, E., Lefevre, S., and Avignon, B. (2020, January 25\u201328). Multispectral fusion for object detection with cyclic fuse-and-refine blocks. Proceedings of the 2020 IEEE International Conference on Image Processing (ICIP), Abu Dhabi, United Arab Emirates.","DOI":"10.1109\/ICIP40778.2020.9191080"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1660","DOI":"10.1109\/TMM.2018.2884481","article-title":"HSCS: Hierarchical sparsity based co-saliency detection for RGBD images","volume":"21","author":"Cong","year":"2018","journal-title":"IEEE Trans. Multimed."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Fu, K., Fan, D.P., Ji, G.P., and Zhao, Q. (2020, January 13\u201319). JL-DCF: Joint learning and densely-cooperative fusion framework for RGB-D salient object detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00312"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1016\/j.neucom.2019.07.012","article-title":"Salient object detection for RGB-D image by single stream recurrent convolution neural network","volume":"363","author":"Liu","year":"2019","journal-title":"Neurocomputing"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Fan, X., Liu, Z., and Sun, G. (2014, January 20\u201323). Salient region detection for stereoscopic images. Proceedings of the 2014 19th International Conference on Digital Signal Processing, Hong Kong, China.","DOI":"10.1109\/ICDSP.2014.6900706"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"55277","DOI":"10.1109\/ACCESS.2019.2913107","article-title":"Adaptive fusion for RGB-D salient object detection","volume":"7","author":"Wang","year":"2019","journal-title":"IEEE Access"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"7012","DOI":"10.1109\/TIP.2020.3028289","article-title":"DPANet: Depth potentiality-aware gated attention network for RGB-D salient object detection","volume":"30","author":"Chen","year":"2020","journal-title":"IEEE Trans. Image Process."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1109\/TCYB.2020.2969255","article-title":"ASIF-Net: Attention steered interweave fusion network for RGB-D salient object detection","volume":"51","author":"Li","year":"2020","journal-title":"IEEE Trans. Cybern."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Ju, R., Ge, L., Geng, W., Ren, T., and Wu, G. (2014, January 27\u201330). Depth saliency based on anisotropic center-surround difference. Proceedings of the 2014 IEEE international conference on image processing (ICIP), Paris, France.","DOI":"10.1109\/ICIP.2014.7025222"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"2075","DOI":"10.1109\/TNNLS.2020.2996406","article-title":"Rethinking RGB-D salient object detection: Models, data sets, and large-scale benchmarks","volume":"32","author":"Fan","year":"2020","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Zhang, M., Ren, W., Piao, Y., Rong, Z., and Lu, H. (2020, January 13\u201319). Select, supplement and focus for RGB-D saliency detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00353"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"107740","DOI":"10.1016\/j.patcog.2020.107740","article-title":"EF-Net: A novel enhancement and fusion network for RGB-D saliency detection","volume":"112","author":"Chen","year":"2021","journal-title":"Pattern Recognit."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Pang, Y., Zhang, L., Zhao, X., and Lu, H. (2020, January 23\u201328). Hierarchical dynamic filtering network for rgb-d salient object detection. Proceedings of the European Conference on Computer Vision, Glasgow, UK.","DOI":"10.1007\/978-3-030-58595-2_15"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"2350","DOI":"10.1109\/TIP.2021.3052069","article-title":"Depth-quality-aware salient object detection","volume":"30","author":"Chen","year":"2021","journal-title":"IEEE Trans. Image Process."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Zhang, W., Jiang, Y., Fu, K., and Zhao, Q. (2021, January 5\u20139). BTS-Net: Bi-directional transfer-and-selection network for RGB-D salient object detection. Proceedings of the 2021 IEEE International Conference on Multimedia and Expo (ICME), Shenzhen, China.","DOI":"10.1109\/ICME51207.2021.9428263"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"3528","DOI":"10.1109\/TIP.2021.3062689","article-title":"Hierarchical alternate interaction network for RGB-D salient object detection","volume":"30","author":"Li","year":"2021","journal-title":"IEEE Trans. Image Process."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"8727","DOI":"10.1109\/TIP.2021.3116793","article-title":"Bifurcated backbone strategy for RGB-D salient object detection","volume":"30","author":"Zhai","year":"2021","journal-title":"IEEE Trans. Image Process."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Hou, Q., Cheng, M.M., Hu, X., Borji, A., Tu, Z., and Torr, P.H. (2017, January 21\u201326). Deeply supervised salient object detection with short connections. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.563"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Chen, H., and Li, Y. (2018, January 18\u201323). Progressively complementarity-aware fusion network for RGB-D salient object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00322"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"2825","DOI":"10.1109\/TIP.2019.2891104","article-title":"Three-stream attention-aware network for RGB-D salient object detection","volume":"28","author":"Chen","year":"2019","journal-title":"IEEE Trans. Image Process."},{"key":"ref_36","unstructured":"Piao, Y., Ji, W., Li, J., Zhang, M., and Lu, H. (November, January 27). Depth-induced multi-scale recurrent attention network for saliency detection. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Seoul, Repulic of Korea."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Liu, Z., Wang, Y., Tu, Z., Xiao, Y., and Tang, B. (2021, January 20\u201324). TriTransNet: RGB-D salient object detection with a triplet transformer embedding network. Proceedings of the 29th ACM International Conference on Multimedia, Chengdu, China.","DOI":"10.1145\/3474085.3475601"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"4873","DOI":"10.1109\/TIP.2020.2976689","article-title":"ICNet: Information conversion network for RGB-D based salient object detection","volume":"29","author":"Li","year":"2020","journal-title":"IEEE Trans. Image Process."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Qin, X., Zhang, Z., Huang, C., Gao, C., Dehghan, M., and Jagersand, M. (2019, January 15\u201320). Basnet: Boundary-aware salient object detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00766"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"6800","DOI":"10.1109\/TIP.2022.3216198","article-title":"CIR-Net: Cross-modality interaction and refinement for RGB-D salient object detection","volume":"31","author":"Cong","year":"2022","journal-title":"IEEE Trans. Image Process."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"10261","DOI":"10.1109\/TPAMI.2021.3134684","article-title":"MobileSal: Extremely efficient RGB-D salient object detection","volume":"44","author":"Wu","year":"2021","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Wu, Z., Su, L., and Huang, Q. (2019, January 15\u201320). Cascaded partial decoder for fast and accurate salient object detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00403"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Liu, J.J., Hou, Q., Cheng, M.M., Feng, J., and Jiang, J. (2019, January 15\u201320). A simple pooling-based design for real-time salient object detection. Proceedings of the IEEE\/CVF Conference on computer VISION and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00404"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Wang, T., Zhang, L., Wang, S., Lu, H., Yang, G., Ruan, X., and Borji, A. (2018, January 18\u201322). Detect globally, refine locally: A novel approach to saliency detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00330"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Woo, S., Park, J., Lee, J.Y., and Kweon, I.S. (2018, January 8\u201314). Cbam: Convolutional block attention module. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., and Torralba, A. (2016, January 27\u201330). Learning deep features for discriminative localization. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.319"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Chen, L., Zhang, H., Xiao, J., Nie, L., Shao, J., Liu, W., and Chua, T.S. (2017, January 21\u201326). Sca-cnn: Spatial and channel-wise attention in convolutional networks for image captioning. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.667"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Liu, S., and Huang, D. (2018, January 8\u201314). Receptive field block net for accurate and fast object detection. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01252-6_24"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Zhao, Z., Xia, C., Xie, C., and Li, J. (2021, January 20\u201324). Complementary trilateral decoder for fast and accurate salient object detection. Proceedings of the 29th ACM International Conference on Multimedia, Chengdu, China.","DOI":"10.1145\/3474085.3475494"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1007\/s10479-005-5724-z","article-title":"A tutorial on the cross-entropy method","volume":"134","author":"Kroese","year":"2005","journal-title":"Ann. Oper. Res."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Peng, H., Li, B., Xiong, W., Hu, W., and Ji, R. (2014, January 6\u201312). RGBD salient object detection: A benchmark and algorithms. Proceedings of the European Conference on Computer Vision, Zurich, Switzerland.","DOI":"10.1007\/978-3-319-10578-9_7"},{"key":"ref_53","unstructured":"Niu, Y., Geng, Y., Li, X., and Liu, F. (2012, January 16\u201321). Leveraging stereopsis for saliency analysis. Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition, Providence, RI, USA."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Cheng, Y., Fu, H., Wei, X., Xiao, J., and Cao, X. (2014, January 10\u201312). Depth enhanced saliency detection method. Proceedings of the International Conference on Internet Multimedia Computing and Service, Xiamen, China.","DOI":"10.1145\/2632856.2632866"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"2321","DOI":"10.1109\/TIP.2022.3154931","article-title":"Dmra: Depth-induced multi-scale recurrent attention network for rgb-d saliency detection","volume":"31","author":"Ji","year":"2022","journal-title":"IEEE Trans. Image Process."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Zhao, J.X., Cao, Y., Fan, D.P., Cheng, M.M., Li, X.Y., and Zhang, L. (2019, January 16\u201317). Contrast prior and fluid pyramid integration for RGBD salient object detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00405"},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Perazzi, F., Kr\u00e4henb\u00fchl, P., Pritch, Y., and Hornung, A. (2012, January 16\u201321). Saliency filters: Contrast based filtering for salient region detection. Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition, Providence, RI, USA.","DOI":"10.1109\/CVPR.2012.6247743"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Fan, D.P., Cheng, M.M., Liu, Y., Li, T., and Borji, A. (2017, January 22\u201329). Structure-measure: A new way to evaluate foreground maps. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.487"},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Achanta, R., Hemami, S., Estrada, F., and Susstrunk, S. (2009, January 20\u201325). Frequency-tuned salient region detection. Proceedings of the 2009 IEEE Conference on Computer Vision and Pattern Recognition, Miami, FL, USA.","DOI":"10.1109\/CVPR.2009.5206596"},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Fan, D.P., Gong, C., Cao, Y., Ren, B., Cheng, M.M., and Borji, A. (2018, January 13\u201319). Enhanced-alignment Measure for Binary Foreground Map Evaluation. Proceedings of the IJCAI, Stockholm, Sweden.","DOI":"10.24963\/ijcai.2018\/97"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"5706","DOI":"10.1109\/TIP.2015.2487833","article-title":"Salient object detection: A benchmark","volume":"24","author":"Borji","year":"2015","journal-title":"IEEE Trans. Image Process."},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., and Fei-Fei, L. (2009, January 20\u201325). Imagenet: A large-scale hierarchical image database. Proceedings of the 2009 IEEE Conference on Computer Vision and Pattern Recognition, Miami, FL, USA.","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref_63","unstructured":"Kingma, D.P., and Ba, J. (2015, January 7\u20139). Adam: A Method for Stochastic Optimization. Proceedings of the ICLR (Poster), San Diego, CA, USA."},{"key":"ref_64","unstructured":"Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., and Antiga, L. (2019). Pytorch: An imperative style, high-performance deep learning library. Adv. Neural Inf. Process. Syst., 32."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"458","DOI":"10.1109\/TIP.2020.3037470","article-title":"Data-level recombination and lightweight fusion scheme for RGB-D salient object detection","volume":"30","author":"Wang","year":"2020","journal-title":"IEEE Trans. Image Process."},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Ji, W., Li, J., Zhang, M., Piao, Y., and Lu, H. (2020, January 23\u201328). Accurate RGB-D salient object detection via collaborative learning. Proceedings of the European Conference on Computer Vision, Glasgow, UK.","DOI":"10.1007\/978-3-030-58523-5_4"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"8407","DOI":"10.1109\/TIP.2020.3014734","article-title":"RGBD salient object detection via disentangled cross-modal fusion","volume":"29","author":"Chen","year":"2020","journal-title":"IEEE Trans. Image Process."},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Piao, Y., Rong, Z., Zhang, M., Ren, W., and Lu, H. (2020, January 13\u201319). A2dele: Adaptive and attentive depth distiller for efficient RGB-D salient object detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00908"},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Zhao, X., Zhang, L., Pang, Y., Lu, H., and Zhang, L. (2020, January 23\u201328). A single stream network for robust and real-time RGB-D salient object detection. Proceedings of the European Conference on Computer Vision, Glasgow, UK.","DOI":"10.1007\/978-3-030-58542-6_39"},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"3376","DOI":"10.1109\/TIP.2021.3060167","article-title":"CDNet: Complementary depth network for RGB-D salient object detection","volume":"30","author":"Jin","year":"2021","journal-title":"IEEE Trans. Image Process."},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Sun, P., Zhang, W., Wang, H., Li, S., and Li, X. (2021, January 20\u201325). Deep RGB-D saliency detection with depth-sensitive attention and automatic multi-modal fusion. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.00146"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/9\/2393\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:28:48Z","timestamp":1760124528000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/9\/2393"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,5,3]]},"references-count":71,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2023,5]]}},"alternative-id":["rs15092393"],"URL":"https:\/\/doi.org\/10.3390\/rs15092393","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,5,3]]}}}