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Time series data has been demonstrated to be crucial in various research fields. The management of large quantities of time series data presents challenges in terms of deep learning tasks, particularly for training a deep neural network. Recently, a ...

Heterogeneous graph neural networks have gained great popularity in tackling various network analysis tasks on heterogeneous network data. However, most existing works mainly focus on general heterogeneous networks, and assume that there is only one ...

Zero-Shot Sketch-Based Image Retrieval (ZS-SBIR) is an innovative cross-modal task that utilizes a sketch to retrieve corresponding images in the zero-shot learning scene. At present, most algorithms treat ZS-SBIR as a typical image classification ...

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 ...

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. However, most existing works ignore the relation ...

Network embedding, which aims at learning low-dimensional representations of nodes in a network, has drawn much attention for various network mining tasks, ranging from link prediction to node classification. In addition to network topological ...

In recent years, heterogeneous network representation learning has attracted considerable attentions with the consideration of multiple node types. However, most of them ignore the rich set of network attributes (attributed network) and different types ...

The detection of abnormal moving objects over high-volume trajectory streams is critical for real time applications ranging from military surveillance to transportation management. Yet this problem remains largely unexplored. In this work, we first ...