2 citations · 3 across the 8 of their papers we have counts for
8 papers
DOD-SA: Infrared-Visible Decoupled Object Detection with Single-Modality Annotations
Hang Jin, Chenqiang Gao, Junjie Guo +4
Infrared-visible object detection has shown great potential in real-world applications, enabling robust all-day perception by leveraging the complementary information of infrared a…
CM-Diff: A Single Generative Network for Bidirectional Cross-Modality Translation Diffusion Model Between Infrared and Visible Images
Bin Hu, Chenqiang Gao, Shurui Liu +4
Image translation is one of the crucial approaches for mitigating information deficiencies in the infrared and visible modalities, while also facilitating the enhancement of modali…
IV-tuning: Parameter-Efficient Transfer Learning for Infrared-Visible Tasks
Yaming Zhang, Chenqiang Gao, Fangcen Liu +4
Existing infrared and visible (IR-VIS) methods inherit the general representations of Pre-trained Visual Models (PVMs) to facilitate complementary learning. However, our analysis i…
IVGF: The Fusion-Guided Infrared and Visible General Framework
Fangcen Liu, Chenqiang Gao, Fang Chen +3
Infrared and visible dual-modality tasks such as semantic segmentation and object detection can achieve robust performance even in extreme scenes by fusing complementary informatio…
DPDETR: Decoupled Position Detection Transformer for Infrared-Visible Object Detection
Junjie Guo, Chenqiang Gao, Fangcen Liu +1
Infrared-visible object detection aims to achieve robust object detection by leveraging the complementary information of infrared and visible image pairs. However, the commonly exi…
DAMSDet: Dynamic Adaptive Multispectral Detection Transformer with Competitive Query Selection and Adaptive Feature Fusion
Junjie Guo, Chenqiang Gao, Fangcen Liu +2
Infrared-visible object detection aims to achieve robust even full-day object detection by fusing the complementary information of infrared and visible images. However, highly dyna…
Are Dense Labels Always Necessary for 3D Object Detection from Point Cloud?
Chenqiang Gao, Chuandong Liu, Jun Shu +5
Current state-of-the-art (SOTA) 3D object detection methods often require a large amount of 3D bounding box annotations for training. However, collecting such large-scale densely-s…
InfMAE: A Foundation Model in the Infrared Modality
Fangcen Liu, Chenqiang Gao, Yaming Zhang +3
In recent years, the foundation models have swept the computer vision field and facilitated the development of various tasks within different modalities. However, it remains an ope…