MOTIVATION: Structural variants (SVs) play an important role in genetic research and precision medicine. As existing SV detection methods usually contain a substantial number of false positive calls, approaches to filter the detection results are needed. RESULTS: We developed a novel deep learning-based SV filtering tool, CSV-Filter, for both short and long reads. CSV-Filter uses a novel multi-level grayscale image encoding method based on CIGAR strings of the alignment results and employs image augmentation techniques to improve SV feature extraction. CSV-Filter also utilizes self-supervised learning networks for transfer as classification models, and employs mixed-precision operations to accelerate training. The experiments showed that the integration of CSV-Filter with popular SV detection tools could considerably reduce false positive SVs for short and long reads, while maintaining true positive SVs almost unchanged. Compared with DeepSVFilter, a SV filtering tool for short reads, CSV-Filter could recognize more false positive calls and support long reads as an additional feature. AVAILABILITY AND IMPLEMENTATION: https://github.com/xzyschumacher/CSV-Filter.

Video-based person re-identification has received considerable attention in recent years due to its significant application in video surveillance. Compared with image-based person re-identification, video-based person re-identification is characterized by a much richer context, which raises the significance of identifying informative regions and fusing the temporal information across frames. In this paper, we propose two relation-guided modules to learn reinforced feature representations for effective re-identification. First, a relation-guided spatial attention (RGSA) module is designed to explore the discriminative regions globally. The weight at each position is determined by its feature as well as the relation features from other positions, revealing the dependence between local and global contents. Based on the adaptively weighted frame-level feature, then, a relation-guided temporal refinement (RGTR) module is proposed to further refine the feature representations across frames. The learned relation information via the RGTR module enables the individual frames to complement each other in an aggregation manner, leading to robust video-level feature representations. Extensive experiments on four prevalent benchmarks verify the state-of-the-art performance of the proposed method.

In video action recognition, motion is a very crucial clue, which is usually represented by optical flow. However, optical flow is computationally expensive to obtain, which becomes the bottleneck for the efficiency of traditional action recognition algorithms. In this article, we propose a network called MV2Flow to learn motion representation efficiently from the signals in the compressed domain. To learn the network, three losses are defined. First, we select the classical TV-L1 flow as proxy ground truth to guide the learning. Besides, an unsupervised image reconstruction loss is proposed to further refine it. Moreover, toward the task of action recognition, the above two losses are combined with a motion content loss. To evaluate our approach, extensive experiments on two benchmark datasets UCF-101 and HMDB-51 are conducted. The motion representation generated with our MV2Flow has shown comparable classification performance on action recognition with TV-L1 flow, while operating at an over 200× faster speed. Based on our MV2Flow and 2D-CNN-based network, we have achieved state-of-the-art performance in the compressed domain. With 3D-CNN-based network, we also achieve comparable accuracy with higher inference speed than methods in the decoded domain setting.