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IEEE TNNLS 2022

Year Title Author Publication Code Tags Notes Datasets
2022 Shattering Distribution for Active Learning Cao and Tsang IEEE TNNLS code New AL method
2022 Active Learning With Multiple Kernels Hong and Chae IEEE TNNLS - stream-based AL framework
2022 Multiview Multi-Instance Multilabel Active Learning Yu et al. IEEE TNNLS Important, code multiview multi-instance multilabel active learning
2022 Active Learning for Deep Visual Tracking Yuan et al. IEEE TNNLS - CNNs, Target Tracking, Representitive Sampling More specifically, to ensure the diversity of selected samples, we propose an active learning method based on multi-frame collaboration to select those training samples that should be and need to be annotated. Meanwhile, considering the representativeness of these selected samples, we adopt a nearest neighbor discrimination method based on the average nearest neighbor distance to screen isolated samples and low- quality samples. OTB100 [1], UAV123 [2], TrackingNet [15], LaSOT [16], GOT10k [17], VOT2019 [41] and VOT2020