58 citations · 77 across the 7 of their papers we have counts for
7 papers
Counterfactual Motion Reliability Learning for Robust UAV Tracking
Yuehai Chen
Infrared unmanned aerial vehicle (UAV) tracking is challenging because the target is often small, low-contrast, and easily confused with thermal distractors or cluttered background…
ConSteer-RL: Steering Reasoning Capabilities in Large Language Models via Confidence-Aware Reinforcement Learning
Qing Miao, Yiming Zhao, Jing Yang +5
Reinforcement Learning from Verifiable Rewards (RLVR) has recently become a key paradigm for improving the reasoning abilities of Large Language Models (LLMs), yet it remains limit…
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…
Counting Varying Density Crowds Through Density Guided Adaptive Selection CNN and Transformer Estimation
Yuehai Chen, Jing Yang, Badong Chen +1
In real-world crowd counting applications, the crowd densities in an image vary greatly. When facing density variation, humans tend to locate and count the targets in low-density r…
Region-Aware Network: Model Human's Top-Down Visual Perception Mechanism for Crowd Counting
Yuehai Chen, Jing Yang, Dong Zhang +3
Background noise and scale variation are common problems that have been long recognized in crowd counting. Humans glance at a crowd image and instantly know the approximate number…