15 citations · 31 across the 11 of their papers we have counts for
11 papers
Learning Causality-inspired Representation Consistency for Video Anomaly Detection
Yang Liu, Zhaoyang Xia, Mengyang Zhao +7
Video anomaly detection is an essential yet challenging task in the multimedia community, with promising applications in smart cities and secure communities. Existing methods attem…
Multi-scale Spatial-temporal Interaction Network for Video Anomaly Detection
Zhiyuan Ning, Zhangxun Li, Zhengliang Guo +2
Video Anomaly Detection (VAD) is an essential yet challenging task in signal processing. Since certain anomalies cannot be detected by isolated analysis of either temporal or spati…
Spatio-Temporal Domain Awareness for Multi-Agent Collaborative Perception
Kun Yang, Dingkang Yang, Jingyu Zhang +6
Multi-agent collaborative perception as a potential application for vehicle-to-everything communication could significantly improve the perception performance of autonomous vehicle…
A novel efficient Multi-view traffic-related object detection framework
Kun Yang, Jing Liu, Dingkang Yang +5
With the rapid development of intelligent transportation system applications, a tremendous amount of multi-view video data has emerged to enhance vehicle perception. However, perfo…
Generalized Video Anomaly Event Detection: Systematic Taxonomy and Comparison of Deep Models
Yang Liu, Dingkang Yang, Yan Wang +5
Video Anomaly Detection (VAD) serves as a pivotal technology in the intelligent surveillance systems, enabling the temporal or spatial identification of anomalous events within vid…
Learning Appearance-motion Normality for Video Anomaly Detection
Yang Liu, Jing Liu, Mengyang Zhao +3
Video anomaly detection is a challenging task in the computer vision community. Most single task-based methods do not consider the independence of unique spatial and temporal patte…
Rethinking Saliency Map: An Context-aware Perturbation Method to Explain EEG-based Deep Learning Model
Hanqi Wang, Xiaoguang Zhu, Tao Chen +2
Deep learning is widely used to decode the electroencephalogram (EEG) signal. However, there are few attempts to specifically investigate how to explain the EEG-based deep learning…
Learning from Attacks: Attacking Variational Autoencoder for Improving Image Classification
Jianzhang Zheng, Fan Yang, Hao Shen +4
Adversarial attacks are often considered as threats to the robustness of Deep Neural Networks (DNNs). Various defending techniques have been developed to mitigate the potential neg…
BiOpt: Bi-Level Optimization for Few-Shot Segmentation
Jinlu Liu, Liang Song, Yongqiang Qin
Few-shot segmentation is a challenging task that aims to segment objects of new classes given scarce support images. In the inductive setting, existing prototype-based methods focu…
Generalized Adaptation for Few-Shot Learning
Liang Song, Jinlu Liu, Yongqiang Qin
Many Few-Shot Learning research works have two stages: pre-training base model and adapting to novel model. In this paper, we propose to use closed-form base learner, which constra…
Prototype Rectification for Few-Shot Learning
Jinlu Liu, Liang Song, Yongqiang Qin
Few-shot learning requires to recognize novel classes with scarce labeled data. Prototypical network is useful in existing researches, however, training on narrow-size distribution…