Area of research
Computer Vision and Pattern Recognition
Research interest
Research interests include Computer science, Artificial intelligence, Computer vision, Feature (linguistics), Fusion, and Reinforcement learning.
The Raman spectroscopy combined with selective state-space algorithm for constructing a rapid disease diagnosis model
TVTracker: Target-Adaptive Text-Guided Visual Fusion for Multimodal RGB-T Tracking
Visual and Textual Commonsense-Enhanced Layout Learning for Vision-and-Language Navigation
Fine-grained hierarchical dynamics for image harmonization
Multimodal separation and cross fusion network based on Raman spectroscopy and FTIR spectroscopy for diagnosis of thyroid malignant tumor metastasis
Quantum Grover Search-Inspired Global Maximum Power Point Tracking for Photovoltaic Systems Under Partial Shading Conditions
Enhancing Scene Understanding for Vision-and-Language Navigation by Knowledge Awareness
Building Robust Video-Level Deepfake Detection via Audio-Visual Local-Global Interactions
Language-Guided Dual-Modal Local Correspondence for Single Object Tracking
Combating Noisy Labels with Sample Selection by Mining High-Discrepancy Examples
Multi-Object Tracking: Decoupling Features to Solve the Contradictory Dilemma of Feature Requirements
Efficient Micro-Expression Spotting Based on Main Directional Mean Optical Flow Feature
Dual-scale point cloud completion network based on high-frequency feature fusion
Emotional Deep Learning Programming Controller for Automatic Voltage Control of Power Systems
Radar Object Detection Using Data Merging, Enhancement and Fusion
Coordinated Complex-Valued Encoding Dragonfly Algorithm and Artificial Emotional Reinforcement Learning for Coordinated Secondary Voltage Control and Automatic Voltage Regulation in Multi-Generator Power Systems
Deep Modular Co-Attention Networks for Visual Question Answering