189 citations · 199 across the 9 of their papers we have counts for
9 papers
STNMamba: Mamba-based Spatial-Temporal Normality Learning for Video Anomaly Detection
Zhangxun Li, Mengyang Zhao, Xuan Yang +6
Video anomaly detection (VAD) has been extensively researched due to its potential for intelligent video systems. However, most existing methods based on CNNs and transformers stil…
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…
Exploiting Spatial-temporal Correlations for Video Anomaly Detection
Mengyang Zhao, Yang Liu, Jing Li +1
Video anomaly detection (VAD) remains a challenging task in the pattern recognition community due to the ambiguity and diversity of abnormal events. Existing deep learning-based VA…
LGN-Net: Local-Global Normality Network for Video Anomaly Detection
Mengyang Zhao, Xinhua Zeng, Yang Liu +4
Video anomaly detection (VAD) has been intensively studied for years because of its potential applications in intelligent video systems. Existing unsupervised VAD methods tend to l…
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…
GPU-accelerated Faster Mean Shift with euclidean distance metrics
Le You, Han Jiang, Jinyong Hu +4
Handling clustering problems are important in data statistics, pattern recognition and image processing. The mean-shift algorithm, a common unsupervised algorithms, is widely used…
Compound Figure Separation of Biomedical Images with Side Loss
Tianyuan Yao, Chang Qu, Quan Liu +11
Unsupervised learning algorithms (e.g., self-supervised learning, auto-encoder, contrastive learning) allow deep learning models to learn effective image representations from large…
ASIST: Annotation-free synthetic instance segmentation and tracking for microscope video analysis
Quan Liu, Isabella M. Gaeta, Mengyang Zhao +6
Instance object segmentation and tracking provide comprehensive quantification of objects across microscope videos. The recent single-stage pixel-embedding based deep learning appr…
Faster Mean-shift: GPU-accelerated clustering for cosine embedding-based cell segmentation and tracking
Mengyang Zhao, Aadarsh Jha, Quan Liu +6
Recently, single-stage embedding based deep learning algorithms gain increasing attention in cell segmentation and tracking. Compared with the traditional "segment-then-associate"…