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Found 13 papers, 9 papers with code
Deep Learning for Trajectory Data Management and Mining: A Survey and Beyond
In this paper, we present a comprehensive review of the development and recent advances in deep learning for trajectory computing (DL4Traj).
Dual-Channel Multiplex Graph Neural Networks for Recommendation
Nevertheless, these approaches still grapple with two significant shortcomings: (1) Insufficient modeling and exploitation of the impact of various behavior patterns formed by multiplex relations between users and items on representation learning, and (2) ignoring the effect of different relations in the behavior patterns on the target relation in recommender system scenarios.
Dataset Condensation for Time Series Classification via Dual Domain Matching
Different from previous methods, our proposed framework aims to generate a condensed dataset that matches the surrogate objectives in both the time and frequency domains.
Multi-scale Traffic Pattern Bank for Cross-city Few-shot Traffic Forecasting
Next, the traffic data of the data-scarce target city could query the traffic pattern bank, facilitating the aggregation of meta-knowledge.
Self-supervised Scene Text Segmentation with Object-centric Layered Representations Augmented by Text Regions
Text segmentation tasks have a very wide range of application values, such as image editing, style transfer, watermark removal, etc. However, existing public datasets are of poor quality of pixel-level labels that have been shown to be notoriously costly to acquire, both in terms of money and time.
Cross-city Few-Shot Traffic Forecasting via Traffic Pattern Bank
Then, the traffic data of the data-scarce city could query the traffic pattern bank and explicit relations between them are constructed.
Trajectory-User Linking via Hierarchical Spatio-Temporal Attention Networks
Trajectory-User Linking (TUL) is crucial for human mobility modeling by linking diferent trajectories to users with the exploration of complex mobility patterns.
Multiplex Heterogeneous Graph Convolutional Network
Heterogeneous graph convolutional networks have gained great popularity in tackling various network analytical tasks on heterogeneous network data, ranging from link prediction to node classification.
Multi-Behavior Hypergraph-Enhanced Transformer for Sequential Recommendation
Further ablation studies validate the effectiveness of our model design and benefits of the new MBHT framework.
Mutual Distillation Learning Network for Trajectory-User Linking
In this paper, we propose a novel Mutual distillation learning network to solve the TUL problem for sparse check-in mobility data, named MainTUL.
Scalable Motif Counting for Large-scale Temporal Graphs
We first categorize the temporal motifs based on their distinct properties, and then design customized algorithms that offer efficient strategies to exactly count the motif instances of each category.
Robust End-to-End Offline Chinese Handwriting Text Page Spotter with Text Kernel
Offline Chinese handwriting text recognition is a long-standing research topic in the field of pattern recognition.
Joint Modeling of Dense and Incomplete Trajectories for Citywide Traffic Volume Inference
In this paper, we propose a novel framework for the citywide traffic volume inference using both dense GPS trajectories and incomplete trajectories captured by camera surveillance systems.
