Ling Shao

TL;DR: A comprehensive review of recent Kinect-based computer vision algorithms and applications covering topics including preprocessing, object tracking and recognition, human activity analysis, hand gesture analysis, and indoor 3-D mapping.

TL;DR: A powerful AGW baseline is designed, achieving state-of-the-art or at least comparable performance on twelve datasets for four different Re-ID tasks, and a new evaluation metric (mINP) is introduced, indicating the cost for finding all the correct matches, which provides an additional criteria to evaluate the Re- ID system for real applications.

TL;DR: Huang et al. as mentioned in this paper improved the Pyramid Vision Transformer (abbreviated as PVTv1) by adding three designs, including overlapping patch embedding, convolutional feed-forward networks and linear complexity attention layers.

TL;DR: This paper proposes a novel filter pruning method by exploring the High Rank of feature maps (HRank), inspired by the discovery that the average rank of multiple feature maps generated by a single filter is always the same, regardless of the number of image batches CNNs receive.