[1] [1] Zhang Z D, Xue Z Y, Chen Y, et al. Boosting verified training f robust image classifications via abstraction[C]Proc of the IEEE Conference on Computer Vision & Pattern Recognition, 2023: 1625116260.

[2] [2] Muhammad F N, Muhammad G Z A K, Xian Y Q, et al. I2mvfmer: large language model generated multiview document supervision f zeroshot image classification[C]Proc of the IEEE Conference on Computer Vision & Pattern Recognition, 2023: 1516915179.

[3] [3] Zhu Y, Tang J, Li S, et al. Derender: intrinsic image decomposition of urban scenes with shape(in)dependent shading rendering[C]Proc of the 2021 IEEE International Conference on Computational Photography (ICCP), 2021: 111.

[4] F Zhang, X Jiang, Z Xia, et al. Non-local color compensation network for intrinsic image decomposition. IEEE Transactions on Circuits and Systems for Video Technology, 33, 132-145(2023).

[5] S Minaee, Y Boykov, F Porikli, et al. Image segmentation using deep learning: a survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44, 3523-3542(2020).

[6] J R Clough, N Byrne, I Oksuz, et al. A topological loss function for deep-learning based image segmentation using persistent homology. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44, 8766-8778(2023).

[7] Dongdong Wang, Wei Zhang, Guofeng Jin, . Application of cusp catastrophic theory in image segmentation of infrared thermal waving inspection. Infrared and Laser Engineering, 43, 1009-1015(2014).

[8] [8] Zou Z, Shi Z, Guo Y, et al. Object detection in 20 years: a survey[J]. Proceedings of the IEEE, 2023, 111(3): 257276.

[9] X Li, C Lv, W Wang, et al. Generalized focal loss: towards efficient representation learning for dense object detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45, 3139-3153(2023).

[10] Y Kong, Y Fu. Human action recognition and prediction: a survey. International Journal of Computer Vision, 130, 1366-1401(2022).

[11] Z Sun, Q Ke, H Rahmani, et al. Human action recognition from various data modalities: A review. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45, 3200-3225(2023).

[12] [12] Seidenschwarz J, Brasó G, Elezi I, et al. Simple cues lead to a strong multiobject tracker[C]Proc of the IEEE Conference on Computer Vision Pattern Recognition, 2023: 1381313823.

[13] W Hu, Q Wang, L Zhang, et al. Siammask: a framework for fast online object tracking and segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45, 3072-3089(2023).

[14] Faling Chen, Qinghai Ding, Haibo Luo, . Anti-occlusion real time target tracking algorithm employing spatio-temporal context. Infrared and Laser Engineering, 50, 20200105(2021).

[15] Bo Li, Xinyu Zhang. Target tracking algorithm based on adaptive feature fusion in complex scenes. Infrared and Laser Engineering, 51, 20220013(2022).

[16] S A Shafer. Using color to separate reflection components. Color Research & Application, 10, 210-218(1985).

[17] Q Yang, J Tang, N Ahuja. Efficient and robust specular highlight removal. IEEE Transactions on Pattern Analysis and Machine Intelligence, 37, 1304-1311(2015).

[18] G Fu, Q Zhang, C Song, et al. Specular highlight removal for real-world images. Computer Graphics Forum, 38, 253-263(2019).

[19] [19] Kim H, Jin H, Hadap S, et al. Specular reflection separation using dark channel pri[C]Proc of the IEEE Conference on Computer Vision & Pattern Recognition, 2013: 14601467.

[20] R T Tan, K Ikeuchi. Separating reflection components of textured surfaces using a single image. IEEE Trans on Pattern Analysis and Machine Intelligence, 27, 178-193(2005).

[21] [21] Liu Y, Yuan Z, Zheng N, et al. Saturationpreserving specular reflection separation[C]Proc of the IEEE Conference on Computer Vision & Pattern Recognition, 2015: 37253733.

[22] J Suo, D An, X Ji, et al. Fast and high quality highlight removal from a single image. IEEE Transactions on Image Processing, 25, 5441-5454(2016).

[23] [23] Yang Q X, Wang S N, Ahuja N. Realtime specular highlight removal using bilateral filtering[C]Proc of the 11th European Conference on Computer Vision, 2010: 87100.

[24] [24] Fu G, Zhang Q, Lin Q, et al. Learning to detect specular highlights from realwld images[C]Proc of the ACM International Conference on Multimedia, 2020: 18731881.

[25] [25] Shi J, Dong Y, Su H, et al. Learning nonlambertian object intrinsics across shape categies[C]Proc of the IEEE Conference on Computer Vision Pattern Recognition, 2017: 16851694.

[26] [26] Yi R, Tan P, Lin S. Leveraging multiview image sets f unsupervised intrinsic image decomposition highlight separation[C]Proc of the AAAI Conference on Artificial Intelligence, 2020: 1268512692.

[27] [27] Huang Z, Hu K, Wang X. M2: multistages specular highlight detection removal in multiscenes [DBOL]. (20220720) [20240220].https:arxiv.dosf.topabs2207.09965.

[28] [28] Hou S, Wang C, Quan W, et al. Textaware single image specular highlight removal[C]Proc of the 4th Chinese Conference on Pattern Recognition Computer Vision (PRCV), 2021: 115127.

[29] [29] JimenezMartin L, Perez D A V, Asteasuainzarra A S M, et al. Specular reflections removal in colposcopic images based on neural wks: Supervised training with no ground truth previous knowledge [DBOL]. (20200621) [20240220].https:doi.g10.48550arXiv.2106.02221.

[30] [30] Fu G, Zhang Q, Zhu L, et al. A multitask wk f joint specular highlight detection removal[C]Proc of the IEEECVF Conference on Computer Vision Pattern Recognition (CVPR), 2021: 77487757.

[31] K Li, Y Wang, J Zhang, et al. Uniformer: unifying convolution and self-attention for visual recognition. IEEE Transactions on Pattern Analysis & Machine Intelligence, 1-18(2023).

[32] [32] Gulati A, Qin J, Chiu C C, et al. Confmer: convolutionaugmented transfmer f speech recognition [DBOL].(20200516)[20240220].https:doi.g10.48550arXiv.2005.08100.

[33] Z H Jiang, W Yu, D Zhou, et al. Convbert: improving bert with span-based dynamic convolution. Advances in Neural Information Processing Systems, 33, 12837-12848(2020).

[34] [34] Wu H, Xiao B, Codella N, et al. Cvt: introducing convolutions to vision transfmers[C]Proc of the IEEECVF International Conference on Computer Vision, 2021: 2231.

[35] C A Saint-Pierre, J Boisvert, G Grimard, et al. Detection and correction of specular reflections for automatic surgical tool segmentation in thoracoscopic images. Machine Vision & Applications, 22, 171-180(2011).

[36] [36] Akashi Y, Okatani T. Separation of reflection components by sparse nonnegative matrix factization[C]Proc of the Asian Conference on Computer Vision, 2015: 611625.

[37] T Yamamoto, A Nakazawa. General improvement method of specular component separation using high-emphasis filter and similarity function. ITE Transactions on Media Technology and Applications, 7, 92-102(2019).