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Heterogeneous graph neural network (HGNN) is a popular technique for modeling and analyzing heterogeneous graphs. Most existing HGNN-based approaches are supervised or semi-supervised learning methods requiring graphs to be annotated, which is costly and ...

Low-light environments will introduce high-intensity noise into images. Containing fine details with reduced noise, near-infrared/flash images can serve as guidance to facilitate noise removal. However, existing fusion-based methods fail to effectively ...

Federated learning has emerged as an effective paradigm to achieve privacy-preserving collaborative learning among different parties. Compared to traditional centralized learning that requires collecting data from each party, in federated ...

In the last decade, the explosive surge in multi-modal data has propelled cross-modal retrieval into the forefront of information retrieval research. Exceptional cross-modal retrieval algorithms are crucial for meeting user requirements ...

Human action understanding (HAU) is a broad topic that involves specific tasks, such as action localisation, recognition, and assessment. However, most popular HAU datasets are bound to one task based on particular actions. Combining different ...

SkatingVerse, a comprehensive benchmark for human action understanding (HAU) tasks is introduced, including action recognition, segmentation, proposal, and assessment. By leveraging figure skating as the task object, SkatingVerse overcomes biases in ...

Graph neural networks (GNNs) have gained popularity in modeling various complex networks, e.g., social network and webpage network. Despite the promising accuracy, the confidences of GNNs are shown to be miscalibrated, indicating limited awareness of ...

Graph neural networks (GNNs) are a type of deep learning models that are trained on graphs and have been successfully applied in various domains. Despite the effectiveness of GNNs, it is still challenging for GNNs to efficiently scale to large graphs. As ...

Graph Neural Networks (GNNs), which obtain node embeddings by attribute propagates along graph topology, exhibit significant power in graph-structured data mining. However, graphs in the real world are usually text-rich, where the text can not only be ...

Semi-supervised learning (SSL) aims to reduce the heavy reliance of current deep models on costly manual annotation by leveraging a large amount of unlabeled data in combination with a much smaller set of labeled data. However, most existing SSL methods ...

Deep cross-media hashing provides an efficient semantic representation learning solution for large-scale cross-media retrieval. The existing methods only consider the inter-media or intra-media semantic association learning, ignore the guiding of semantic ...

Time series forecasting plays a crucial part in many real-world applications. Recent studies have proven the power of Transformer to model long-range dependency for time series forecasting tasks. Nevertheless, the quadratic computational ...

The past decade has seen rapid growth of distributed stream data processing systems. Under these systems, a stream application is realized as a Directed Acyclic Graph (DAG) of operators, where the level of parallelism of each operator has a substantial ...

Scholar clustering has garnered increasing attention due to the explosive growth of scholar data. Although researchers have proposed many algorithms to cluster scholars, they typically focus on clustering scholars from the intrinsic view (scholars’ ...

Recently, the wanton dissemination of fake news on social media has adversely affected our lives, rendering automatic fake news detection a pressing issue. Current methods are often fully supervised and typically employ deep neural networks (DNN) to ...

Sarcasm is widely utilized on social media platforms such as Twitter and Reddit. Sarcasm detection is required for analyzing people's true feelings since sarcasm is commonly used to portray a reversed emotion opposing the literal meaning. The syntactic ...

During the training process of federated learning models, the domain information of the target test data on the server can differ greatly from the training data of each client, leading to a decrease in the performance of the federated model. Additionally, ...

Traffic prediction is an important component of the intelligent transportation system. Existing deep learning methods encode temporal information and spatial information separately or iteratively. However, the spatial and temporal information is highly ...

Recently, progress has been made towards improving automatic sarcasm detection in computer science. Among existing models, manually constructing static graphs for texts and then using graph neural networks (GNNs) is one of the most effective approaches ...

Graph Neural Networks (GNNs) have been a prevailing technique for tackling various analysis tasks on graph data. A key premise for the remarkable performance of GNNs relies on complete and trustworthy initial graph descriptions (i.e., node features and ...

Machine learning (ML) has been recently introduced to solving optimization problems, especially for combinatorial optimization (CO) tasks. In this paper, we survey the trend of leveraging ML to solve the mixed-integer programming ...