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The Tenth Visual Object Tracking VOT2022 Challenge Results.

M. Kristan, A. Leonardis, J. Matas, M. Felsberg, R. Pflugfelder, J. Kämäräinen, H. Chang, M. Danelljan, L. Zajc, A. Lukezic, O. Drbohlav, J. Björklund, Y. Zhang, Z. Zhang, S. Yan, W. Yang, D. Cai, C. Mayer, G. Fernández, K. Ben, G. Bhat, H. Chang, G. Chen, J. Chen, S. Chen, X. Chen, X. Chen, X. Chen, Y. Chen, Y. Chen, Z. Chen, Y. Cheng, A. Ciaramella, Y. Cui, B. Dzubur, M. Dasari, Q. Deng, D. Dhar, S. Di, E. Nardo, D. Du, M. Dunnhofer, H. Fan, Z. Feng, Z. Fu, S. Gao, R. Gorthi, E. Granger, Q. Gu, H. Gupta, J. He, K. He, Y. Huang, D. Jangid, R. Ji, C. Jiang, Y. Jiang, F. Lawin, Z. Kang, M. Kiran, J. Kittler, S. Lai, X. Lan, D. Lee, H. Lee, S. Lee, H. Li, M. Li, W. Li, X. Li, X. Li, X. Li, Z. Li, L. Lin, H. Ling, B. Liu, C. Liu, S. Liu, H. Lu, R. Cruz, B. Ma, C. Ma, J. Ma, Y. Ma, N. Martinel, A. Memarmoghadam, C. Micheloni, P. Moallem, L. Nguyen-Meidine, S. Pan, C. Park, D. Paudel, M. Paul, H. Peng, A. Robinson, L. Rout, S. Shan, K. Simonato, T. Song, X. Song, C. Sun, J. Sun, Z. Tang, R. Timofte, C. Tsai, L. Gool, O. Verma, D. Wang, F. Wang, L. Wang, L. Wang, L. Wang, L. Wang, Q. Wang, G. Wu, J. Wu, X. Wu, F. Xie, T. Xu, W. Xu, Y. Xu, Y. Xu, W. Xue, Z. Xun, B. Yan, D. Yang, J. Yang, W. Yang, X. Yang, Y. Yang, Y. Yang, Z. Yang, B. Ye, F. Yu, H. Yu, J. Yu, Q. Yu, W. Yu, K. Ze, J. Zhai, C. Zhang, C. Zhang, K. Zhang, T. Zhang, W. Zhang, Z. Zhang, Z. Zhang, J. Zhao, S. Zhao, F. Zheng, H. Zheng, M. Zheng, B. Zhong, J. Zhu, X. Zhu, and Y. Zhuang. ECCV Workshops (8), volume 13808 of Lecture Notes in Computer Science, page 431-460. Springer, (2022)

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Martin Danelljan

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Accurate 3D Object Detection using Energy-Based Models.F. Gustafsson, M. Danelljan, and T. Schön. CoRR, (2020)How Reliable is Your Regression Model's Uncertainty Under Real-World Distribution Shifts?F. Gustafsson, M. Danelljan, and T. Schön. CoRR, (2023)AVisT: A Benchmark for Visual Object Tracking in Adverse Visibility.M. Noman, W. Ghallabi, D. Najiha, C. Mayer, A. Dudhane, M. Danelljan, H. Cholakkal, S. Khan, L. Gool, and F. Khan. CoRR, (2022)GOCor: Bringing Globally Optimized Correspondence Volumes into Your Neural Network.P. Truong, M. Danelljan, L. Gool, and R. Timofte. NeurIPS, (2020)Probabilistic Regression for Visual Tracking.M. Danelljan, L. Gool, and R. Timofte. CVPR, page 7181-7190. Computer Vision Foundation / IEEE, (2020)Evaluating Scalable Bayesian Deep Learning Methods for Robust Computer Vision.F. Gustafsson, M. Danelljan, and T. Schön. CVPR Workshops, page 1289-1298. Computer Vision Foundation / IEEE, (2020)Probabilistic Warp Consistency for Weakly-Supervised Semantic Correspondences.P. Truong, M. Danelljan, F. Yu, and L. Gool. CVPR, page 8698-8708. IEEE, (2022)Mask Transfiner for High-Quality Instance Segmentation.L. Ke, M. Danelljan, X. Li, Y. Tai, C. Tang, and F. Yu. CVPR, page 4402-4411. IEEE, (2022)NTIRE 2022 Challenge on Learning the Super-Resolution Space.A. Lugmayr, M. Danelljan, R. Timofte, K. Kim, Y. Kim, J. Lee, Z. Li, J. Pan, D. Shim, K. Song and 3 other author(s). CVPR Workshops, page 785-796. IEEE, (2022)Dense Gaussian Processes for Few-Shot Segmentation.J. Johnander, J. Edstedt, M. Felsberg, F. Khan, and M. Danelljan. ECCV (29), volume 13689 of Lecture Notes in Computer Science, page 217-234. Springer, (2022)

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Disambiguation of "Danelljan, Martin"

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The Tenth Visual Object Tracking VOT2022 Challenge Results.

Please choose a person to relate this publication to

Martin Danelljan

Martin Klug

Martin Brückner

Martin Breitenfeldt

Martin Faes

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Disambiguation

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Disambiguation of "Danelljan, Martin"

copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML The Tenth Visual Object Tracking VOT2022 Challenge Results.

Please choose a person to relate this publication to

Martin Danelljan

Martin Klug

Martin Brückner

Martin Breitenfeldt

Martin Faes

Other publications of authors with the same name

Disambiguation

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The Tenth Visual Object Tracking VOT2022 Challenge Results.