Papers
6
Total Citations
39
H-Index
4
About
Jialong Xie is a researcher at the forefront of intelligent robotics, specializing in computer vision, human-robot interaction, and agricultural automation. His work centers on enabling robots to perceive and interact with their environments more naturally and precisely. Xie’s major contributions include developing PD-YOLO (12 citations), a novel weed detection method using multi-scale feature fusion for sustainable agriculture, and pioneering continual learning techniques for robotic grasping that allow knowledge transfer across tasks (8 citations). He has advanced language-conditioned segmentation and grasping by integrating multisource heterogeneous knowledge (7 citations), enabling robots to follow complex human instructions. His dynamic head gesture recognition method (6 citations) facilitates real-time intention inference for visual human-robot interaction, while his hierarchical multi-modal fusion approach (4 citations) improves grasping detection in cluttered environments. With over 39 total citations across his most cited works, Xie’s research bridges the gap between perception and action, making robots more adaptable in industrial, household, and agricultural settings. His innovative coarse-to-fine feature refinement networks further enhance grasp detection precision, establishing him as a rising figure in robotic manipulation and vision-based automation.
Research Focus
Key Achievements
Top Papers
- 1PD-YOLO: a novel weed detection method based on multi-scale feature fusion12 citations · 2025
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