12 citations · 27 across the 6 of their papers we have counts for
6 papers
SIAVC: Semi-Supervised Framework for Industrial Accident Video Classification
Zuoyong Li, Qinghua Lin, Haoyi Fan +2
Semi-supervised learning suffers from the imbalance of labeled and unlabeled training data in the video surveillance scenario. In this paper, we propose a new semi-supervised learn…
FireMatch: A Semi-Supervised Video Fire Detection Network Based on Consistency and Distribution Alignment
Qinghua Lin, Zuoyong Li, Kun Zeng +3
Deep learning techniques have greatly enhanced the performance of fire detection in videos. However, video-based fire detection models heavily rely on labeled data, and the process…
Contrastive Proposal Extension with LSTM Network for Weakly Supervised Object Detection
Pei Lv, Suqi Hu, Tianran Hao
Weakly supervised object detection (WSOD) has attracted more and more attention since it only uses image-level labels and can save huge annotation costs. Most of the WSOD methods u…
Deep Dual Support Vector Data Description for Anomaly Detection on Attributed Networks
Fengbin Zhang, Haoyi Fan, Ruidong Wang +2
Networks are ubiquitous in the real world such as social networks and communication networks, and anomaly detection on networks aims at finding nodes whose structural or attributed…
AnomalyDAE: Dual autoencoder for anomaly detection on attributed networks
Haoyi Fan, Fengbin Zhang, Zuoyong Li
Anomaly detection on attributed networks aims at finding nodes whose patterns deviate significantly from the majority of reference nodes, which is pervasive in many applications su…
Correlation-aware Deep Generative Model for Unsupervised Anomaly Detection
Haoyi Fan, Fengbin Zhang, Ruidong Wang +2
Unsupervised anomaly detection aims to identify anomalous samples from highly complex and unstructured data, which is pervasive in both fundamental research and industrial applicat…