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Learning a Confidence Measure for Optical Flow.

, , , and . IEEE Trans. Pattern Anal. Mach. Intell., 35 (5): 1107-1120 (2013)

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Co-dependence Aware Fuzzing for Dataflow-Based Big Data Analytics., , and . ESEC/SIGSOFT FSE, page 1050-1061. ACM, (2023)Video Segmentation by Tracking Many Figure-Ground Segments., , , , and . ICCV, page 2192-2199. IEEE Computer Society, (2013)NaturalFuzz: Natural Input Generation for Big Data Analytics., , , and . ASE, page 1592-1603. IEEE, (2023)Learning to find occlusion regions, , and . IEEE CVPR, page 2161--2168. (2011)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).The Middle Child Problem: Revisiting Parametric Min-Cut and Seeds for Object Proposals., , and . ICCV, page 1600-1608. IEEE Computer Society, (2015)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)Learning a confidence measure for optical flow., , , and . IEEE TPAMI, 35 (5): 1107--20 (2013)Learning a Confidence Measure for Optical Flow, , , and . IEEE Transactions on Pattern Analysis and Machine Intelligence (To Appear), (2012)Learning a Confidence Measure for Optical Flow., , , and . IEEE Trans. Pattern Anal. Mach. Intell., 35 (5): 1107-1120 (2013)