3 citations · 3 across the 3 of their papers we have counts for
4 papers · 1 filter
Salience DETR: Enhancing Detection Transformer with Hierarchical Salience Filtering Refinement
Xiuquan Hou, Meiqin Liu, Senlin Zhang +2
DETR-like methods have significantly increased detection performance in an end-to-end manner. The mainstream two-stage frameworks of them perform dense self-attention and select a…
IA-LSTM: Interaction-Aware LSTM for Pedestrian Trajectory Prediction
Yuehai Chen
Predicting the trajectory of pedestrians in crowd scenarios is indispensable in self-driving or autonomous mobile robot field because estimating the future locations of pedestrians…
Learning Discriminative Features for Crowd Counting
Yuehai Chen, Qingzhong Wang, Jing Yang +3
Crowd counting models in highly congested areas confront two main challenges: weak localization ability and difficulty in differentiating between foreground and background, leading…
Tolerating Annotation Displacement in Dense Object Counting via Point Annotation Probability Map
Yuehai Chen, Jing Yang, Badong Chen +2
Counting objects in crowded scenes remains a challenge to computer vision. The current deep learning based approach often formulate it as a Gaussian density regression problem. Suc…