[2] [2] BERTINETTO L, VALMADRE J, HENRIQUES J F, et al. Fully-convolutional Siamese networks for object tracking［C］//European Conference on Computer Vision. Cham: Springer, 2016: 850-865.

[3] [3] CHEN Z, ZHONG B, LI G, et al. Siamese box adaptive network for visual tracking［C］//Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Seattle: IEEE, 2020: 6667-6676.

[4] [4] HAN K, WANG Y, TIAN Q, et al. GhostNet: more features from cheap operations［C］//Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Seattle: IEEE, 2020: 1577-1586.

[5] [5] LI B, WU W, WANG Q, et al. SiamRPN++: evolution of Siamese visual tracking with very deep networks［C］//Proceedings of the 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Long Beach: IEEE, 2019: 4277-4286.

[6] [6] CUI Y, JIANG C, WANG L, et al. MixFormer: end-to-end tracking with iterative mixed attention［C］//Proceedings of the 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition. New Orleans: IEEE, 2022: 13598-13608.

[7] [7] KENDALL A, GAL Y, CIPOLLA R. Multi-task learning using uncertainty to weigh losses for scene geometry and semantics［C］//Proceedings of the 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City: IEEE, 2018: 7482-7491.

[8] [8] FAN H, LIN L T, YANG F, et al. LaSOT: a high-quality benchmark for large-scale single object tracking［C］//Proceedings of the 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Long Beach: IEEE, 2019: 5369-5378.

[9] [9] MULLER M, BIBI A, GIANCOLA S, et al. TrackingNet: a large-scale dataset and benchmark for object tracking in the wild［C］//European Conference on Computer Vision. Cham: Springer, 2018: 310-327.

[10] [10] LIN T Y, MAIRE M, BELONGIE S, et al. Microsoft COCO: common objects in context［C］//European Conference on Computer Vision. Cham: Springer, 2014: 740-755.

[11] [11] HUANG L H, ZHAO X, HUANG K Q. GOT-10k: a large high-diversity benchmark for generic object tracking in the wild［J］. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021, 43(5): 1562-1577.

[12] [12] MUELLER M, SMITH N, GHANEM B. A benchmark and simulator for UAV tracking［C］//European Conference on Computer Vision. Cham: Springer, 2016: 445-461.

[13] [13] LIN T Y, DOLLR P, GIRSHICK R, et al. Feature pyramid networks for object detection［C］//Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition. Honolulu: IEEE, 2017: 936-944.

[14] [14] FU Z H, FU Z H, LIU Q J, et al. SparseTT: visual tracking with sparse Transformers［C］//Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence. Vienna: IJCAI, 2022: 905-912.

[15] [15] WEI X, BAI Y F, ZHENG Y C, et al. Autoregressive visual tracking［C］//Proceedings of the 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Vancouver: IEEE, 2023: 9697-9706.

[16] [16] GUO D Y, WANG J, CUI Y, et al. SiamCAR: Siamese fully convolutional classification and regression for visual tracking［C］//Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Seattle: IEEE, 2020: 6268-6276.

[17] [17] CAO Z A, FU C H, YE J J, et al. HiFT: hierarchical feature Transformer for aerial tracking［C］//Proceedings of the 2021 IEEE/CVF International Conference on Computer Vision. ［S. l.］: IEEE, 2021: 15457-15466.

[18] [18] CAO Z A, FU C H, YE J J, et al. SiamAPN++: Siamese attentional aggregation network for real-time UAV tracking［C］//Proceedings of the 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems. Prague: IEEE, 2021: 3086-3092.

[19] [19] CAO Z A, HUANG Z Y, PAN L, et al. TCTrack: temporal contexts for aerial tracking［C］//Proceedings of the 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition. New Orleans: IEEE, 2022: 14778-14788.

[20] [20] GUO D Y, SHAO Y Y, CUI Y, et al. Graph attention tracking［C］//Proceedings of the 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Nashville: IEEE, 2021: 9538-9547.

[21] [21] CUI Y T, SONG T H, WU G S, et al. MixFormerV2: efficient fully Transformer tracking［C］//Proceedings of the 37th Conference on Neural Information Processing Systems. Red Hook: Curran Associates Inc. , 2023: 58736-58751.