Area of research
Computer Vision and Pattern Recognition · Artificial Intelligence
Research interest
Research interests include Computer science, Artificial intelligence, Segmentation, Object (grammar), Point cloud, and Object detection.
PD-1 regulates the anti-tumor immune function of macrophages through JAK2-STAT3 signaling pathway in colorectal cancer tumor microenvironment
Construction of a risk factor prediction model for postoperative complications in elderly patients with colorectal cancer using machine learning
The selective 3e− ORR pathway induced by differential polarization of surface -OH by adjacent heterodinuclear metals realizes the directed conversion of radicals
Referred by Multi-Modality: A Unified Temporal Transformer for Video Object Segmentation
Toward the unification of generative and discriminative visual foundation model: a survey
CLIP-Adapter: Better Vision-Language Models with Feature Adapters
UniFormer: Unifying Convolution and Self-Attention for Visual Recognition
LoGoNet: Towards Accurate 3D Object Detection with Local-to-Global Cross- Modal Fusion
Rethinking Range View Representation for LiDAR Segmentation
CLIP2Scene: Towards Label-efficient 3D Scene Understanding by CLIP
Efficient Image Super-Resolution Using Vast-Receptive-Field Attention
Learning Open-Vocabulary Semantic Segmentation Models From Natural Language Supervision
Uni3D: A Unified Baseline for Multi-Dataset 3D Object Detection
DetZero: Rethinking Offboard 3D Object Detection with Long-term Sequential Point Clouds
ResFormer: Scaling ViTs with Multi-Resolution Training
Blueprint Separable Residual Network for Efficient Image Super-Resolution
Blind Image Super-Resolution: A Survey and Beyond
Dual-AI: Dual-path Actor Interaction Learning for Group Activity Recognition
Reflash Dropout in Image Super-Resolution
Deep learning to diagnose Hashimoto’s thyroiditis from sonographic images
Preparation of ultrashort composite nanotubes by twin-screw extruder
Asynchronous feature regularization and cross-modal distillation for OCT based glaucoma diagnosis
Affordance Transfer Learning for Human-Object Interaction Detection
Detecting Human-Object Interaction via Fabricated Compositional Learning
Tripartite Information Mining and Integration for Image Matting
NTIRE 2021 Challenge on Perceptual Image Quality Assessment
Deep Relation Transformer for Diagnosing Glaucoma With Optical Coherence Tomography and Visual Field Function
Visual Compositional Learning for Human-Object Interaction Detection
AIM 2020 Challenge on Efficient Super-Resolution: Methods and Results
Development and clinical deployment of a smartphone-based visual field deep learning system for glaucoma detection