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Learning a Confidence Measure for Optical Flow

, , , and . IEEE Transactions on Pattern Analysis and Machine Intelligence (To Appear), (2012)

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NaturalFuzz: Natural Input Generation for Big Data Analytics., , , and . ASE, page 1592-1603. IEEE, (2023)Video Segmentation by Tracking Many Figure-Ground Segments., , , , and . ICCV, page 2192-2199. IEEE Computer Society, (2013)Co-dependence Aware Fuzzing for Dataflow-Based Big Data Analytics., , and . ESEC/SIGSOFT FSE, page 1050-1061. ACM, (2023)Detection and Incremental Object Learning in Videos.. Georgia Institute of Technology, Atlanta, GA, USA, (2019)base-search.net (ftgeorgiatech:oai:smartech.gatech.edu:1853/61614).Learning to find occlusion regions, , and . IEEE CVPR, page 2161--2168. (2011)The Middle Child Problem: Revisiting Parametric Min-Cut and Seeds for Object Proposals., , and . ICCV, page 1600-1608. IEEE Computer Society, (2015)Incremental Object Learning From Contiguous Views., , , , , , , and . CVPR, page 8777-8786. Computer Vision Foundation / IEEE, (2019)Learning to find occlusion regions., , and . CVPR, page 2161-2168. IEEE Computer Society, (2011)A Novel Paradigm for Mining Cell Phenotypes in Multi-tag Bioimages Using a Locality Preserving Nonlinear Embedding., , , , and . ICONIP (4), volume 7666 of Lecture Notes in Computer Science, page 575-583. Springer, (2012)Natural Symbolic Execution-Based Testing for Big Data Analytics., , , and . Proc. ACM Softw. Eng., 1 (FSE): 2677-2700 (2024)