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The Tenth Visual Object Tracking VOT2022 Challenge Results.

, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , and . ECCV Workshops (8), volume 13808 of Lecture Notes in Computer Science, page 431-460. Springer, (2022)

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The Lovász Hinge: A Convex Surrogate for Submodular Losses., and . CoRR, (2015)Yes, IoU loss is submodular - as a function of the mispredictions., , , and . CoRR, (2018)A Convex Surrogate Operator for General Non-Modular Loss Functions., and . AISTATS, volume 51 of JMLR Workshop and Conference Proceedings, page 1032-1041. JMLR.org, (2016)HIMap: HybrId Representation Learning for End-to-end Vectorized HD Map Construction., , , , , , and . CoRR, (2024)Learning Submodular Losses with the Lovasz Hinge., and . ICML, volume 37 of JMLR Workshop and Conference Proceedings, page 1623-1631. JMLR.org, (2015)An Efficient Decomposition Framework for Discriminative Segmentation with Supermodular Losses., and . CoRR, (2017)Learning Generalized Intersection Over Union for Dense Pixelwise Prediction., , , , , , and . ICML, volume 139 of Proceedings of Machine Learning Research, page 12198-12207. PMLR, (2021)The Eighth Visual Object Tracking VOT2020 Challenge Results., , , , , , , , , and 98 other author(s). ECCV Workshops (5), volume 12539 of Lecture Notes in Computer Science, page 547-601. Springer, (2020)AFOD: Adaptive Focused Discriminative Segmentation Tracker., , , , , and . ECCV Workshops (5), volume 12539 of Lecture Notes in Computer Science, page 666-682. Springer, (2020)Efficient Learning for Discriminative Segmentation with Supermodular Losses., and . BMVC, BMVA Press, (2016)