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BREEDS: Benchmarks for Subpopulation Shift [98.90314444545204]
We develop a methodology for assessing the robustness of models to subpopulation shift. We leverage the class structure underlying existing datasets to control the data subpopulations that comprise the training and test distributions. Applying this methodology to the ImageNet dataset, we create a suite of subpopulation shift benchmarks of varying granularity.
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Alpha-Refine: Boosting Tracking Performance by Precise Bounding Box Estimation [87.53808756910452]
We propose a novel, flexible and accurate refinement module called Alpha-Refine. It exploits a precise pixel-wise correlation layer together with a spatial-aware non-local layer to fuse features and can predict three complementary outputs: bounding box, corners and mask. We apply the proposed Alpha-Refine module to five famous and state-of-the-art base trackers: DiMP, ATOM, SiamRPN++, RTMDNet and ECO.
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Unsupervised Learning Consensus Model for Dynamic Texture Videos Segmentation [12.462608802359936]
We present an effective unsupervised learning consensus model for the segmentation of dynamic texture (ULCM) In the proposed model, the set of values of the requantized local binary patterns (LBP) histogram around the pixel to be classified are used as features. Experiments conducted on the challenging SynthDB dataset show that ULCM is significantly faster, easier to code, simple and has limited parameters.
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