Zobrazeno 1 - 10
of 225
pro vyhledávání: '"Peng, Qinmu"'
Autor:
Chen, Jiazhen, Fu, Sichao, Zhang, Zhibin, Ma, Zheng, Feng, Mingbin, Wirjanto, Tony S., Peng, Qinmu
Few-shot graph anomaly detection (GAD) has recently garnered increasing attention, which aims to discern anomalous patterns among abundant unlabeled test nodes under the guidance of a limited number of labeled training nodes. Existing few-shot GAD ap
Externí odkaz:
http://arxiv.org/abs/2410.08629
Generalized additive models (GAM) have been successfully applied to high dimensional data analysis. However, most existing methods cannot simultaneously estimate the link function, the component functions and the variable interaction. To alleviate th
Externí odkaz:
http://arxiv.org/abs/2410.06012
Fine-grained recognition, a pivotal task in visual signal processing, aims to distinguish between similar subclasses based on discriminative information present in samples. However, prevailing methods often erroneously focus on background areas, negl
Externí odkaz:
http://arxiv.org/abs/2408.01998
The challenge in fine-grained visual categorization lies in how to explore the subtle differences between different subclasses and achieve accurate discrimination. Previous research has relied on large-scale annotated data and pre-trained deep models
Externí odkaz:
http://arxiv.org/abs/2309.08097
In recent years, graph neural networks (GNN) have achieved significant developments in a variety of graph analytical tasks. Nevertheless, GNN's superior performance will suffer from serious damage when the collected node features or structure relatio
Externí odkaz:
http://arxiv.org/abs/2309.02762
With the fast development of AI-related techniques, the applications of trajectory prediction are no longer limited to easier scenes and trajectories. More and more heterogeneous trajectories with different representation forms, such as 2D or 3D coor
Externí odkaz:
http://arxiv.org/abs/2304.05106
Few-shot class-incremental learning (FSCIL) has recently attracted extensive attention in various areas. Existing FSCIL methods highly depend on the robustness of the feature backbone pre-trained on base classes. In recent years, different Transforme
Externí odkaz:
http://arxiv.org/abs/2303.15494
Filter pruning has gained widespread adoption for the purpose of compressing and speeding up convolutional neural networks (CNNs). However, existing approaches are still far from practical applications due to biased filter selection and heavy computa
Externí odkaz:
http://arxiv.org/abs/2303.03645
Graph neural networks (GNNs) with missing node features have recently received increasing interest. Such missing node features seriously hurt the performance of the existing GNNs. Some recent methods have been proposed to reconstruct the missing node
Externí odkaz:
http://arxiv.org/abs/2302.08250
Hashing that projects data into binary codes has shown extraordinary talents in cross-modal retrieval due to its low storage usage and high query speed. Despite their empirical success on some scenarios, existing cross-modal hashing methods usually f
Externí odkaz:
http://arxiv.org/abs/2209.12599