17 citations · 38 across the 8 of their papers we have counts for
8 papers
Gated Cross-Attention Network for Depth Completion
Xiaogang Jia, Songlei Jian, Yusong Tan +3
Depth completion is a popular research direction in the field of depth estimation. The fusion of color and depth features is the current critical challenge in this task, mainly due…
RoSAS: Deep Semi-Supervised Anomaly Detection with Contamination-Resilient Continuous Supervision
Hongzuo Xu, Yijie Wang, Guansong Pang +3
Semi-supervised anomaly detection methods leverage a few anomaly examples to yield drastically improved performance compared to unsupervised models. However, they still suffer from…
Fascinating Supervisory Signals and Where to Find Them: Deep Anomaly Detection with Scale Learning
Hongzuo Xu, Yijie Wang, Juhui Wei +3
Due to the unsupervised nature of anomaly detection, the key to fueling deep models is finding supervisory signals. Different from current reconstruction-guided generative models a…
Calibrated One-class Classification for Unsupervised Time Series Anomaly Detection
Hongzuo Xu, Yijie Wang, Songlei Jian +3
Time series anomaly detection is instrumental in maintaining system availability in various domains. Current work in this research line mainly focuses on learning data normality de…
DRAM Failure Prediction in AIOps: Empirical Evaluation, Challenges and Opportunities
Zhiyue Wu, Hongzuo Xu, Guansong Pang +4
DRAM failure prediction is a vital task in AIOps, which is crucial to maintain the reliability and sustainable service of large-scale data centers. However, limited work has been d…
Hierarchical Adaptive Pooling by Capturing High-order Dependency for Graph Representation Learning
Ning Liu, Songlei Jian, Dongsheng Li +3
Graph neural networks (GNN) have been proven to be mature enough for handling graph-structured data on node-level graph representation learning tasks. However, the graph pooling te…
Rethinking Class Relations: Absolute-relative Supervised and Unsupervised Few-shot Learning
Hongguang Zhang, Piotr Koniusz, Songlei Jian +2
The majority of existing few-shot learning methods describe image relations with binary labels. However, such binary relations are insufficient to teach the network complicated rea…
STA: Adversarial Attacks on Siamese Trackers
Xugang Wu, Xiaoping Wang, Xu Zhou +1
Recently, the majority of visual trackers adopt Convolutional Neural Network (CNN) as their backbone to achieve high tracking accuracy. However, less attention has been paid to the…