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Expanding object detector's Horizon: Incremental learning framework for object detection in videos
2015
2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Over the last several years it has been shown that imagebased object detectors are sensitive to the training data and often fail to generalize to examples that fall outside the original training sample domain (e.g., videos). A number of domain adaptation (DA) techniques have been proposed to address this problem. DA approaches are designed to adapt a fixed complexity model to the new (e.g., video) domain. We posit that unlabeled data should not only allow adaptation, but also improve (or at
doi:10.1109/cvpr.2015.7298597
dblp:conf/cvpr/KuznetsovaHRS15
fatcat:yx2ao3zzpfe7ni4zv5cjfcn4gm