At present, occlusion and similar appearance pose serious challenges to the task of person re-identification. In this work, we propose an efficient multi-scale channel attention network (EMCA) to learn robust and more discriminative features to solve these problems. Specifically, we designed a novel cross-channel attention module (CCAM) in EMCA and placed it after different layers in the backbone. The CCAM includes local cross-channel interaction (LCI) and channel weight integration (CWI). LCI focuses on both the maximum pooling features and the average pooling features to generate channel weights through convolutional layers, respectively. CWI combines the two channel weights to generate richer and more discriminant channel weights. Experiments on four popular person Re-ID datasets (Market-1501, DukeMTMC-ReID, CUHK-03 (detected) and MSMT17) show that the performance of our EMCA is consistently significantly superior to the existing state-of-the-art methods.

Accurate prediction of demand for traditional taxi and ride-hailing services is crucial for addressing supply-demand imbalances. However, recent studies based on global adaptive graphs, local spatial-temporal graphs, and self-attention mechanisms struggle to effectively capture the dynamic and intricate relations in demand. Moreover, existing dynamic graph generators face challenges in efficiently producing high-quality graphs to learn the diverse interactions among zones along time axis and their shared patterns spanning various time scales. To solve these challenges, we propose a novel Local-Perception-Enhanced Spatial-Temporal Evolving Graph Transformer Network (LPE-STGTN), aimed at improving the effectiveness and efficiency of extracting intricate local dependencies in taxi demand. Specifically, we elaborately design a spatial-temporal evolving graph generator to absorb shared and diversified inter-zone relations across different temporal periodicities and specific interactions among zones within each time step. Furthermore, an attention free transformer with local context (AFT-local) is introduced to effectively learn the correlations between adjacent time steps. Extensive experiments on three taxi datasets of New York and Beijing are carried out to evaluate the superior performance of our model. Compared with the most competitive baseline, our model achieves a balance between effectiveness and efficiency on three datasets, with average training time reduction of 70.66% and average performance improvement of 1.96%.

This article presents a novel matching optimization algorithm for low-altitude remote sensing images based on a geometrical constraint and a convolutional neural network (CNN). The proposed method was designed to be effective in enhancing the integrity and accuracy of point clouds generated by stereo matching. To overcome the limitations of stereo matching, we trained a CNN to predict how well image patches match and used it in patch optimization. The main advantage of this approach is that the proposed algorithm can decrease the mismatching and errors caused by noise, deep discontinuity, and weak texture in low-altitude remote sensing images and can reconstruct an integrated and accurate point cloud. Comparative studies and experimental results validate the accuracy of the proposed algorithm when used for dense point generation from low-altitude remote sensing images.