Area of research
Computer Vision and Pattern Recognition · Automotive Engineering
Research interest
Research interests include Computer science, Artificial intelligence, Computer vision, Perception, Convolutional neural network, and Domain (mathematical analysis).
Robust Multi-task Adversarial Attacks Using Min-max Optimization
Light the Night: A Multi-Condition Diffusion Framework for Unpaired Low-Light Enhancement in Autonomous Driving
Retracted: Sora-Based Parallel Vision for Smart Sensing of Intelligent Vehicles: From Foundation Models to Foundation Intelligence
Domain Adaptation Based Object Detection for Autonomous Driving in Foggy and Rainy Weather
Abductive Ego-View Accident Video Understanding for Safe Driving Perception
Adversarial Relighting Against Face Recognition
V2V4Real: A Real-World Large-Scale Dataset for Vehicle-to-Vehicle Cooperative Perception
Learning for Vehicle-to-Vehicle Cooperative Perception Under Lossy Communication
Domain Adaptive Object Detection for Autonomous Driving under Foggy Weather
Bridging the Domain Gap for Multi-Agent Perception
Convolutional Neural Networks Based Remote Sensing Scene Classification under Clear and Cloudy Environments
Deep Domain Adaptation Based Multi-Spectral Salient Object Detection
Multi-Spectral Salient Object Detection by Adversarial Domain Adaptation
Domain Adaptation for Convolutional Neural Networks-Based Remote Sensing Scene Classification
Degraded Image Semantic Segmentation With Dense-Gram Networks
An easy-to-hard learning strategy for within-image co-saliency detection
Small Object Sensitive Segmentation of Urban Street Scene With Spatial Adjacency Between Object Classes