[1] [1] XU H, ZHANG H, MA J Y. Classification saliency-based rule for visible and infrared image fusion[J]. IEEE Transactions on Computational Imaging, 2021, 7: 824-836.

[2] [2] LI G Y, WANG Y K, LIU Z, et al. RGB-T semantic segmentation with location, activation, and sharpening[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2023, 33(3): 1223-1235.

[4] [4] Itti L, Koch C, Niebur E. A model of saliency-based visual attention for rapid scene analysis[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1998, 20(11): 1254-1259.

[5] [5] LI C L, CHENG H, HU S Y, et al. Learning collaborative sparse representation for grayscale-thermal tracking[J]. IEEE Transactions on Image Processing, 2016, 25(12): 5743-5756.

[7] [7] WANG G Z, LI C L, MA Y P, et al. RGB-T saliency detection benchmark: dataset, baselines, analysis and a novel approach[C]//IGTA 2018: The 13th Academic Conference on Image Graphics Technology and Application, 2018: 359-369.

[8] [8] MA Y, SUN D, MENG Q, et al. Learning multiscale deep features and svm regressors for adaptive RGB-T saliency detection[C]//ISCID 2017: 2017 10th International Symposium on Computational Intelligence and Design, 2017: 389-392.

[9] [9] ZHOU D Y, Weston J, Gretton A, et al. Ranking on data manifolds[C]//NIPS 2003: Advances in Neural Information Processing Systems, 2003: 169-176.

[10] [10] TU Z Z, XIA T, LI C L, et al. M3S-NIR: multi-modal multi-scale noise-insensitive ranking for RGB-T saliency detection[C]//MIPR 2019: 2019 IEEE Conference on Multimedia Information Processing and Retrieval, 2019: 141-146.

[11] [11] HUANG L M, SONG K C, WANG J, et al. Multi-graph fusion and learning for RGBT image saliency detection[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2022, 32(3): 1366-1377.

[12] [12] HUANG L M, SONG K C, GONG A J, et al. RGB-T saliency detection via low-rank tensor learning and unified collaborative ranking[J]. IEEE Signal Processing Letters, 2020, 27: 1585-1589.

[14] [14] Sandler M, Howard A, ZHU M L, et al. MobileNetV2: inverted residuals and linear bottlenecks[C]//CVPR 2018: Proceedings of the 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018: 4510-4520.

[15] [15] TU Z Z, XIA T, LI C L, et al. RGB-t image saliency detection via collaborative graph learning[J]. IEEE Transactions on Multimedia, 2020, 22(1): 160-173.

[16] [16] PANG Y, WU H, WU C D. Cross-modal co-feedback cellular automata for RGB-T saliency detection[J]. Pattern Recognition, 2023, 135: 109-138.

[17] [17] LIU Z Y, HUANG X S, ZHANG G H et al. Scribble-supervised RGB-T salient object detection[C]//ICME 2023: Proceedings of the IEEE International Conference on Multimedia and Expo, 2023: 2369-2374.

[18] [18] ZHANG Q, HUANG N C, YAO L, et al. RGB-T salient object detection via fusing multi-level CNN features[J]. IEEE Transactions on Image Processing, 2020, 29: 3321-3335.

[19] [19] ZHANG Q, HUANG N C, XIAO T, et al. Revisiting feature fusion for RGB-T salient object detection[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2020, 31(5): 1804-1818.

[20] [20] BI H B, WU R W, LIU Z Q, et al. PSNet: parallel symmetric network for RGB-T salient object detection[J]. Neurocomputing, 2022, 511: 410-425.

[21] [21] TU Z Z, MA Y, LI Z, et al. RGBT salient object detection: a large-scale dataset and benchmark[J]. IEEE Transactions on Multimedia, 2022, 25: 4163-4176.

[22] [22] TU Z Z, LI Z, LI C L, et al. Multi-interactive dual-decoder for RGB-thermal salient object detection[J]. IEEE Transactions on Image Processing, 2021, 30: 5678-5691.

[23] [23] WANG J, SONG K C, BAO Y Q, et al. CGFNet: cross-guided fusion network for RGB-T salient object detection[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2022, 32(5): 2949-2961.

[24] [24] CHEN Q, LIU Z, ZHANG Y, et al. RGB-D Salient Object Detection via 3D Convolutional Neural Networks[C]//Proceedings of the AAAI Conference on Artificial Intelligence, 2022: 1063-1071.

[25] [25] CHEN G, SHAO F, CHAI X L, et al. CGMDRNet: cross-guided modality difference reduction network for RGB-T salient object detection[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2022, 32(9): 6308-6323.

[26] [26] LIAO G B, GAO W, LI G, et al. Cross-collaborative fusion-encoder network for robust rgb-thermal salient object detection[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2022, 32(11): 7646-7661.

[27] [27] CONG R M, ZHANG K P, ZHANG C, et al. Does thermal really always matter for RGB-T salient object detection?[J]. IEEE Transactions on Multimedia, 2022, 25: 1-12.

[28] [28] LIANG Y H, QIN G H, SUN M H, et al. Multi-modal interactive attention and dual progressive decoding network for RGB-D/T salient object detection[J]. Neurocomputing, 2022, 490: 132-145.

[29] [29] GAO W, LIAO G B, MA S W, et al. Unified information fusion network for multi-modal RGB-D and RGB-T salient object detection[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2022, 32(4): 2091-2106.

[30] [30] PANG Y W, ZHAO X Q, ZHANG L H, et al. CAVER: cross-modal view-mixed transformer for bi-modal salient object detection[J]. IEEE Transactions on Image Processing, 2023, 32: 892-904.

[31] [31] ZHOU W J, GUO Q L, LEI J S, et al. ECFFNet: effective and consistent feature fusion network for RGB-T salient object detection[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2022, 32(3): 1224-1235.

[32] [32] ZHOU W J, ZHU Y, LEI J S, et al. LSNet: lightweight spatial boosting network for detecting salient objects in RGB-thermal images[J]. IEEE Transactions on Image Processing, 2023, 32: 1329-1340.

[33] [33] Vaswani A, Shazeer N, Parmar N, et al. Attention is all you need[C]//NIPS 2017: Advances in Neural Information Processing Systems, 2017: 6000-6010.

[34] [34] WANG W H, XIE E Z, LI X, et al. PVTv2: Improved baselines with pyramid vision transformer[J]. Computational Visual Media, 2021, 8: 415-424.

[35] [35] LIU Z Y, TAN Y C, HE Q, et al. SwinNet: swin transformer drives edge-aware RGB-D and RGB-T salient object detection[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2022, 32(7): 4486-4497.

[36] [36] CHEN G, SHAO F, CHAI X L, et al. Modality-induced transfer-fusion network for RGB-D and RGB-T salient object detection[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2023, 33(4): 1787-1801.

[37] [37] TANG B, LIU Z Y, TAN Y C, et al. HRTransNet: HRFormer-driven two-modality salient object detection[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2023, 33(2): 728-742.

[38] [38] YUAN Y H, FU R, HUANG L, et al. HRFormer: high-resolution vision transformer for dense predict[C]//NIPS 2021: Advances in Neural Information Processing Systems, Virtual, 2021: 7281-7293.

[39] [39] FAN D P, CHENG M M, LIU Y, et al. Structure-measure: a new way to evaluate foreground maps[C]//ICCV 2017: Proceedings of the 2017 IEEE/CVF International Conference on Computer Vision, 2017: 4558-4567.

[40] [40] FAN D P, GONG C, CAO Y, et al. Enhanced-alignment measure for binary foreground map evaluation[C]//IJCAI 2018: The 27th International Joint Conference on Artificial Intelligence, 2018: 698-704.

[41] [41] YAN Q, XU L, SHI J P, et al. Hierarchical saliency detection[C]//CVPR 2013: Proceedings of the 2013 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2013: 1155-1162.

[42] [42] LIN Y, HOU X D, Koch C, et al. The secrets of salient object segmentation[C]//CVPR 2014: Proceedings of the 2014 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2014: 280-287.