Titlebook: Neural Information Processing; 30th International C Biao Luo,Long Cheng,Chaojie Li Conference proceedings 2024 The Editor(s) (if applicable

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OD-Enhanced Dynamic Spatial-Temporal Graph Convolutional Network for?Metro Passenger Flow Predictionfirst challenge is extracting the diverse passenger flow patterns at different stations, e.g., stations near residential areas and stations near commercial areas, while the second one is to model the complex dynamic spatial-temporal correlations caused by Origin-Destination (OD) flows. Existing stud

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Enhancing Heterogeneous Graph Contrastive Learning with?Strongly Correlated Subgraphsews of a graph, obtaining feature representations for graph data in an unsupervised manner without the need for manual labeling. Most existing node-level graph contrastive learning models only consider embeddings of the same node in different views as positive sample pairs, ignoring rich inherent ne

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MFSFFuse: Multi-receptive Field Feature Extraction for?Infrared and?Visible Image Fusion Using Self-litate subsequent visual tasks. Most of the current fusion methods suffer from incomplete feature extraction or redundancy, resulting in indistinctive targets or lost texture details. Moreover, the infrared and visible image fusion lacks ground truth, and the fusion results obtained by using unsuper

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Progressive Temporal Transformer for?Bird’s-Eye-View Camera Pose Estimation focus on ground view in indoor or outdoor scenes. However, camera relocalization on unmanned aerial vehicles is less focused. Also, frequent view changes and a large depth of view make it more challenging. In this work, we establish a Bird’s-Eye-View (BEV) dataset for camera relocalization, a large

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Stereo Visual Mesh for?Generating Sparse Semantic Maps at?High Frame Ratesroduces a Visual Mesh based stereo vision method for sparse stereo semantic segmentation. A dataset of simulated 3D scenes was generated and used for training to show that the method is capable of processing high resolution stereo inputs to generate both left and right sparse semantic maps. The new

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Exploring Adaptive Regression Loss and?Feature Focusing in?Industrial Scenarioss vary greatly-for example, the variety of texture shapes and the complexity of background information. A lightweight Focus Encoder-Decoder Network (FEDNet) is presented to solve these problems. Specifically, the novelty of FEDNet is as follows: First, the feature focusing module (FFM) is designed t

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Optimal Task Grouping Approach in?Multitask Learningrove the performance of the other tasks. However, the tasks are not always constructive on each other in the multi-task formulation and might play negatively during the training process leading to poor results. Thus, this study focuses on finding the optimal group of tasks that should be trained tog