78 citations · 171 across the 13 of their papers we have counts for
13 papers
Focus the Discrepancy: Intra- and Inter-Correlation Learning for Image Anomaly Detection
Xincheng Yao, Ruoqi Li, Zefeng Qian +2
Humans recognize anomalies through two aspects: larger patch-wise representation discrepancies and weaker patch-to-normal-patch correlations. However, the previous AD methods didn'…
Towards Diverse Temporal Grounding under Single Positive Labels
Hao Zhou, Chongyang Zhang, Yanjun Chen +1
Temporal grounding aims to retrieve moments of the described event within an untrimmed video by a language query. Typically, existing methods assume annotations are precise and uni…
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.…
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…
Embracing Uncertainty: Decoupling and De-bias for Robust Temporal Grounding
Hao Zhou, Chongyang Zhang, Yan Luo +2
Temporal grounding aims to localize temporal boundaries within untrimmed videos by language queries, but it faces the challenge of two types of inevitable human uncertainties: quer…
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…