RGBT论文参考

1Chen X, Yan B, Zhu J, et al. Transformer tracking C//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition(CVPR). New Orleans, LA, USA: IEEE, 2021: 8126-8135.

2Li C, Wu X, Zhao N, et al. Fusing two-stream convolutional neural networks for RGB-T object trackingJ. Neurocomputing, 2018, 281: 78-85.

3Wang X, Shu X, Zhang S, et al. MFGNet: Dynamic modality-aware filter generation for RGB-T trackingJ. IEEE Transactions on Multimedia, 2022,25:4335 - 4348.

4Zhang P, Wang D, Lu H, et al. Learning adaptive attribute-driven representation for real-time RGB-T trackingJ. International Journal of Computer Vision, 2021, 129: 2714-2729.

5Li C L, Lu A, Zheng A H, et al. Multi-adapter rgbt tracking C//2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW). Seoul, Korea (South): IEEE, 2019: 2262-2270.

6Lu A, Li C, Yan Y, et al. RGBT tracking via multi-adapter network with hierarchical divergence loss C//Proceedings of the IEEE Transactions on Image Processing. Piscataway, NJ, USA: IEEE, 2021: 5613-5625.

7Xiao Y, Yang M, Li C, et al. Attribute-based progressive fusion network for rgbt tracking C//Proceedings of the AAAI Conference on Artificial Intelligence. Vancouver, BC, Canada: AAAI Press, 2022, 36(3): 2831-2838.

8Mei J , Zhou D , Cao J , et al. HDINet: hierarchical dual-sensor interaction network for RGBT tracking J. IEEE Sensors Journal, 2021(21-15): 16915-16926.

9Yu Y, Xiong Y, Huang W, et al. Deformable siamese attention networks for visual object trackingC//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2020: 6728-6737.

10Bertinetto L, Valmadre J, Henriques J F, et al. Fully-convolutional siamese networks for object trackingC//Computer Vision--ECCV 2016 Workshops: Amsterdam, The Netherlands, October 8-10 and 15-16, 2016, Proceedings, Part II 14. Springer International Publishing, 2016: 850-865.

11Wang Q, Teng Z, Xing J, et al. Learning attentions: residual attentional siamese network for high performance online visual trackingC//Proceedings of the IEEE conference on computer vision and pattern recognition. 2018: 4854-4863.

12Feng M, Su J. Learning reliable modal weight with transformer for robust RGBT trackingJ. Knowledge-Based Systems, 2022, 249: 108945-108958.

13Cai Y, Sui X, Gu G, et al. Learning modality feature fusion via transformer for RGBT-trackingJ. Infrared Physics & Technology, 2023, 133: 104819-104828.

14Lan X, Ye M, Zhang S, et al. Robust collaborative discriminative learning for RGB-infrared trackingC//Proceedings of the AAAI Conference on Artificial Intelligence. 2018, 32(1).

15Lan X, Ye M, Shao R, et al. Online non-negative multi-modality feature template learning for RGB-assisted infrared trackingJ. IEEE Access, 2019, 7: 67761-67771.

16Liu H P, Sun F C. Fusion tracking in color and infrared images using joint sparse representationJ. Science China Information Sciences, 2012, 55: 590-599.

17Luo C, Sun B, Yang K, et al. Thermal infrared and visible sequences fusion tracking based on a hybrid tracking framework with adaptive weighting schemeJ. Infrared Physics & Technology, 2019, 99: 265-276.

18Zhang P, Zhao J, Bo C, et al. Jointly modeling motion and appearance cues for robust RGB-T trackingJ. IEEE Transactions on Image Processing, 2021, 30: 3335-3347.

19Li C, Cheng H, Hu S, et al. Learning collaborative sparse representation for grayscale-thermal tracking J. IEEE Transactions on Image Processing, 2016, 25(12): 5743-5756.

20Li C, Liang X, Lu Y, et al. RGB-T object tracking: benchmark and baseline J. Pattern Recognition, 2019, 96: 106977-106989.

21Li C, Xue W, Jia Y, et al. Lasher: a large-scale high-diversity benchmark for RGBT tracking J. IEEE Transactions on Image Processing, 2022, 31: 392-404.

22Gao Y, Li C, Zhu Y, et al. Deep adaptive fusion network for high performance RGBT tracking C//Proceedings of the IEEE International Conference on Computer Vision Workshops(ICCVW). Seoul, Korea (South): IEEE, 2019: 91-99.

23Zhu Y, Li C, Luo B, et al. Dense feature aggregation and pruning for RGBT tracking C//Proceedings of the ACM International Conference on Multimedia. Nice, France: ACM, 2019: 465-472.

24Li C L, Zhao N, Lu Y J, Zhu C L, Tang J. Weighted sparse representation regularized graph learning for RGB-T object tracking C//Proceedings of the 25th ACM International Conference on Multimedia. Mountain View, CA, USA: ACM, 2017: 1856-1864.

25Danelljan M, Bhat G, Shahbaz Khan F, et al. ECO: efficient convolution operators for tracking C//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition(CVPR). Honolulu, HI, USA: IEEE, 2017: 6638-6646.

26Danelljan M, Robinson A, Shahbaz Khan F, et al. Beyond correlation filters: learning continuous convolution operators for visual tracking C//Computer Vision--ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11-14, 2016, Proceedings, Part V 14. Springer International Publishing, 2016: 472-488.

27Nam H, Han B. Real-time MDNet C//Proceedings of the 15th European Conference on Computer Vision(ECCV). Munich, Germany: Springer, 2018: 89-104.

28Lu A, Qian C, Li C, Tang J, Wang L. Duality-gated mutual condition network for RGBT tracking J. IEEE Transactions on Neural Networks and Learning Systems, 2022,1-14.

29Zhang H, Zhang L, Zhuo L, Zhang J. Object tracking in RGB-T videos using modal-aware attention network and competitive learning J. Sensors, 2020, 20(2): 393.

30Zhang L, Danelljan M, Gonzalez-Garcia A, van de Weijer J, Shahbaz Khan F. Multi-modal fusion for end-to-end RGB-T tracking C//2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW). Seoul, Korea (South): IEEE, 2019: 2252-2261.

31Zhu Y, Li C, Tang J, et al. Quality-aware feature aggregation network for robust RGBT trackingJ. IEEE Transactions on Intelligent Vehicles, 2020, 6(1): 121-130.

32Zhu Y, Li C, Tang J, et al. RGBT tracking by trident fusion networkJ. IEEE Transactions on Circuits and Systems for Video Technology, 2021, 32(2): 579-592.

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