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…