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Fine-grained classification of identity document types with only one example.

, , and . MVA, page 126-129. IEEE, (2015)

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Predicting Landscapes from Environmental Conditions Using Generative Networks., , , , and . GCPR, volume 11824 of Lecture Notes in Computer Science, page 203-217. Springer, (2019)MB-Net: MergeBoxes for Real-Time 3D Vehicles Detection., , , , and . Intelligent Vehicles Symposium, page 2117-2124. IEEE, (2018)Learning with few examples for binary and multiclass classification using regularization of randomized trees., and . Pattern Recognit. Lett., 32 (2): 244-251 (2011)Enhanced anomaly detection in wire ropes by combining structure and appearance., and . Pattern Recognit. Lett., 34 (8): 942-953 (2013)One-Class Classification for Anomaly Detection in Wire Ropes with Gaussian Processes in a Few Lines of Code., , , and . MVA, page 219-222. (2011)Generic Hierarchic Object Models and Classification Based on Probabilistic PCA., , and . MVA, page 435-438. (2002)Active Learning for Regression Tasks with Expected Model Output Changes., , , , , and . BMVC, page 103. BMVA Press, (2018)Emergent Techniques and Applications for Big Visual Data., , , , and . Int. J. Digit. Multim. Broadcast., (2017)Extrinsic Self-Calibration of Time-of-Flight Cameras using a Combination of 3D and Intensity Descriptors., , and . VMV, page 269-276. Eurographics Association, (2011)On the Suitability of Different Features for Anomaly Detection in Wire Ropes., , , , and . VISIGRAPP (Selected Papers), volume 68 of Communications in Computer and Information Science, page 296-308. Springer, (2009)