Area of research
Computer Vision and Pattern Recognition · Aerospace Engineering
Research interest
Research interests include Robotics and Sensor-Based Localization, Robotic Locomotion and Control, Robotic Path Planning Algorithms, and Advanced Image and Video Retrieval Techniques.
NavComposer: Composing Language Instructions for Navigation Trajectories Through Action-Scene-Object Modularization
Reinforcement Learning-Based Whole-Body Motion Control for Humanoids With Position-Controlled Joints
Efficient Text-Driven Motion Generation via Latent Consistency Training
VLLM-LAD: Visual Large Language Model for Zero-shot Logical Anomaly Detection
Fully Exploiting Vision Foundation Model's Profound Prior Knowledge for Generalizable RGB-Depth Driving Scene Parsing
A&B-LO: Continuous-Time LiDAR Odometry With Adaptive Non-Uniform B-Spline Trajectory Representation
BEVDrive-E2E: Imitation With Bird's Eye View Perception for Interpretable End-to-End Autonomous Driving
DREAM: Dynamic routing of experts via attention-based mixture for vision-language-action modeling
TerrFlat: Physics-Driven Geometry Representation for Structure-Aware Freespace Detection
Representation learning for skeleton-based action recognition from a causal perspective
RoadFormer+: Delivering RGB-X Scene Parsing Through Scale-Aware Information Decoupling and Advanced Heterogeneous Feature Fusion
Online, Target-Free LiDAR-Camera Extrinsic Calibration via Cross-Modal Mask Matching
SNE-RoadSegV2: Advancing Heterogeneous Feature Fusion and Fallibility Awareness for Freespace Detection
A multilevel attention network with sub-instructions for continuous vision-and-language navigation
Contrastive Feedback Vision-Language for 3D Skeleton-Based Action Recognition
Three-Filters-to-Normal+: Revisiting Discontinuity Discrimination in Depth-to-Normal Translation
Self-Supervised Point Cloud Importance Awareness Network for 2-D LiDAR SLAM
Temporal Scene-Object Graph Learning for Object Navigation
Distributed Fault Detection of Autonomous Vehicle Networks Using Local Relative Measurements
SG-RoadSeg+: End-to-End Freespace Detection Upgraded at Data, Feature, and Loss Levels
FoggyDepth: Leveraging Channel Frequency and Non-Local Features for Depth Estimation in Fog
These Maps Are Made by Propagation: Adapting Deep Stereo Networks to Road Scenarios With Decisive Disparity Diffusion.
DCPI-Depth: Explicitly Infusing Dense Correspondence Prior to Unsupervised Monocular Depth Estimation.
LIX: Implicitly Infusing Spatial Geometric Prior Knowledge Into Visual Semantic Segmentation for Autonomous Driving.
Beyond Histogram Comparison: Distribution-Aware Simple-Path Graph Kernels
RoadFormer: Duplex Transformer for RGB-Normal Semantic Road Scene Parsing
Playing to Vision Foundation Model's Strengths in Stereo Matching
S$^{3}$M-Net: Joint Learning of Semantic Segmentation and Stereo Matching for Autonomous Driving
Evolutionary Decision-Making and Planning for Autonomous Driving: A Hybrid Augmented Intelligence Framework
CLIPose: Category-Level Object Pose Estimation With Pre-Trained Vision-Language Knowledge