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Multi-label learning is often applied to handle complex decision tasks, and feature selection is its essential part. The relation of labels is always ignored or not enough to consider for both multi-label learning and its feature selection. To deal with the problem, F-neighborhood rough sets are employed. Different from other methods, the original approximate space is not changed, but the relation of labels is sufficient to consider. To be specific, a multi-label decision system is discomposed into a family of singlelabel decision tables with the label set(first-order strategy) at first. Secondly, calculate attribute significance in the family of single-label decision tables. Third, construct an attribute significance matrix and improved attribute significance matrices to evaluate the quality of the features, then a parallel reduct is obtained with information fusion. These processes construct F-neighborhood parallel reduction algorithm for a multi-label decision system(FNPRMS). Compared with the state-of-the-arts, experimental results show that FNPRMS is effective and efficient on 9 publicly available data sets.

Siamese network-based trackers consider tracking as features cross-correlation between the target template and the search region. Therefore, feature representation plays an important role for constructing a high-performance tracker. However, all existing Siamese networks extract the deep but low-resolution features of the entire patch, which is not robust enough to estimate the target bounding box accurately. In this work, to address this issue, we propose a novel high-resolution Siamese network, which connects the high-to-low resolution convolution streams in parallel as well as repeatedly exchanges the information across resolutions to maintain high-resolution representations. The resulting representation is semantically richer and spatially more precise by a simple yet effective multi-scale feature fusion strategy. Moreover, we exploit attention mechanisms to learn object-aware masks for adaptive feature refinement, and use deformable convolution to handle complex geometric transformations. This makes the target more discriminative against distractors and background. Without bells and whistles, extensive experiments on popular tracking benchmarks containing OTB100, UAV123, VOT2018 and LaSOT demonstrate that the proposed tracker achieves state-of-the-art performance and runs in real time, confirming its efficiency and effectiveness.

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