Zobrazeno 1 - 10
of 189
pro vyhledávání: '"Pang Guansong"'
Graph Transformers (GTs) have demonstrated remarkable performance in incorporating various graph structure information, e.g., long-range structural dependency, into graph representation learning. However, self-attention -- the core module of GTs -- p
Externí odkaz:
http://arxiv.org/abs/2411.17296
Visual anomaly detection targets to detect images that notably differ from normal pattern, and it has found extensive application in identifying defective parts within the manufacturing industry. These anomaly detection paradigms predominantly focus
Externí odkaz:
http://arxiv.org/abs/2411.09558
One key challenge in Out-of-Distribution (OOD) detection is the absence of ground-truth OOD samples during training. One principled approach to address this issue is to use samples from external datasets as outliers (i.e., pseudo OOD samples) to trai
Externí odkaz:
http://arxiv.org/abs/2410.20807
Graph anomaly detection (GAD), which aims to identify nodes in a graph that significantly deviate from normal patterns, plays a crucial role in broad application domains. Existing GAD methods, whether supervised or unsupervised, are one-model-for-one
Externí odkaz:
http://arxiv.org/abs/2410.14886
Autor:
Zhao, Sinong, Wang, Wenrui, Xu, Hongzuo, Yu, Zhaoyang, Wen, Qingsong, Wang, Gang, Liu, xiaoguang, Pang, Guansong
Identifying anomalies from time series data plays an important role in various fields such as infrastructure security, intelligent operation and maintenance, and space exploration. Current research focuses on detecting the anomalies after they occur,
Externí odkaz:
http://arxiv.org/abs/2410.12206
Class-incremental learning (CIL) aims to continually learn a sequence of tasks, with each task consisting of a set of unique classes. Graph CIL (GCIL) follows the same setting but needs to deal with graph tasks (e.g., node classification in a graph).
Externí odkaz:
http://arxiv.org/abs/2410.10341
Current zero-shot anomaly detection (ZSAD) methods show remarkable success in prompting large pre-trained vision-language models to detect anomalies in a target dataset without using any dataset-specific training or demonstration. However, these meth
Externí odkaz:
http://arxiv.org/abs/2410.10289
Graph anomaly detection (GAD), which aims to identify unusual graph instances (nodes, edges, subgraphs, or graphs), has attracted increasing attention in recent years due to its significance in a wide range of applications. Deep learning approaches,
Externí odkaz:
http://arxiv.org/abs/2409.09957
Video anomaly detection (VAD) aims to discover behaviors or events deviating from the normality in videos. As a long-standing task in the field of computer vision, VAD has witnessed much good progress. In the era of deep learning, with the explosion
Externí odkaz:
http://arxiv.org/abs/2409.05383
Autor:
Wu, Peng, Zhou, Xuerong, Pang, Guansong, Yang, Zhiwei, Yan, Qingsen, Wang, Peng, Zhang, Yanning
Current weakly supervised video anomaly detection (WSVAD) task aims to achieve frame-level anomalous event detection with only coarse video-level annotations available. Existing works typically involve extracting global features from full-resolution
Externí odkaz:
http://arxiv.org/abs/2408.05905