14 citations · 20 across the 4 of their papers we have counts for
5 papers · 1 filter
Learning to Predict Trustworthiness with Steep Slope Loss
Yan Luo, Yongkang Wong, Mohan S. Kankanhalli +1
Understanding the trustworthiness of a prediction yielded by a classifier is critical for the safe and effective use of AI models. Prior efforts have been proven to be reliable on…
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…
-Reference Transfer Learning for Saliency Prediction
Yan Luo, Yongkang Wong, Mohan S. Kankanhalli +1
Benefiting from deep learning research and large-scale datasets, saliency prediction has achieved significant success in the past decade. However, it still remains challenging to p…