14 citations · 19 across the 3 of their papers we have counts for
3 papers
cs.CV2022★ 4 cited
Explicit Boundary Guided Semi-Push-Pull Contrastive Learning for Supervised Anomaly Detection
Xincheng Yao, Ruoqi Li, Jing Zhang +2
Most anomaly detection (AD) models are learned using only normal samples in an unsupervised way, which may result in ambiguous decision boundary and insufficient discriminability.…
cs.CV2021★ 1 cited
Which to Match? Selecting Consistent GT-Proposal Assignment for Pedestrian Detection
Yan Luo, Chongyang Zhang, Muming Zhao +2
Accurate pedestrian classification and localization have received considerable attention due to their wide applications such as security monitoring, autonomous driving, etc. Althou…
cs.CV2020★ 14 cited
Where, What, Whether: Multi-modal Learning Meets Pedestrian Detection
Yan Luo, Chongyang Zhang, Muming Zhao +2
Pedestrian detection benefits greatly from deep convolutional neural networks (CNNs). However, it is inherently hard for CNNs to handle situations in the presence of occlusion and…