most citedA Memory-Augmented Multi-Task Collaborative Framework for Unsupervised Traffic Accident Detection in Driving Videos

3 citations · 7 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CV20243 cited

Image Copy-Move Forgery Detection via Deep PatchMatch and Pairwise Ranking Learning

Yuanman Li, Yingjie He, Changsheng Chen +4

Recent advances in deep learning algorithms have shown impressive progress in image copy-move forgery detection (CMFD). However, these algorithms lack generalizability in practical…

cs.CV2024

Text-Driven Traffic Anomaly Detection with Temporal High-Frequency Modeling in Driving Videos

Rongqin Liang, Yuanman Li, Jiantao Zhou +1

Traffic anomaly detection (TAD) in driving videos is critical for ensuring the safety of autonomous driving and advanced driver assistance systems. Previous single-stage TAD method…

cs.CV20231 cited

Multi-scale Target-Aware Framework for Constrained Image Splicing Detection and Localization

Yuxuan Tan, Yuanman Li, Limin Zeng +3

Constrained image splicing detection and localization (CISDL) is a fundamental task of multimedia forensics, which detects splicing operation between two suspected images and local…

cs.CV2023

Image Copy-Move Forgery Detection via Deep Cross-Scale PatchMatch

Yingjie He, Yuanman Li, Changsheng Chen +1

The recently developed deep algorithms achieve promising progress in the field of image copy-move forgery detection (CMFD). However, they have limited generalizability in some prac…

cs.CV20233 cited

A Memory-Augmented Multi-Task Collaborative Framework for Unsupervised Traffic Accident Detection in Driving Videos

Rongqin Liang, Yuanman Li, Yingxin Yi +2

Identifying traffic accidents in driving videos is crucial to ensuring the safety of autonomous driving and driver assistance systems. To address the potential danger caused by the…