15 citations · 19 across the 10 of their papers we have counts for
10 papers
EvaNet: Towards More Efficient and Consistent Infrared and Visible Image Fusion Assessment
Chunyang Cheng, Tianyang Xu, Xiao-Jun Wu +4
Evaluation is essential in image fusion research, yet most existing metrics are directly borrowed from other vision tasks without proper adaptation. These traditional metrics, ofte…
Beyond Strict Pairing: Arbitrarily Paired Training for High-Performance Infrared and Visible Image Fusion
Yanglin Deng, Tianyang Xu, Chunyang Cheng +3
Infrared and visible image fusion(IVIF) combines complementary modalities while preserving natural textures and salient thermal signatures. Existing solutions predominantly rely on…
Serial Over Parallel: Learning Continual Unification for Multi-Modal Visual Object Tracking and Benchmarking
Zhangyong Tang, Tianyang Xu, Xuefeng Zhu +4
Unifying multiple multi-modal visual object tracking (MMVOT) tasks draws increasing attention due to the complementary nature of different modalities in building robust tracking sy…
Omni Survey for Multimodality Analysis in Visual Object Tracking
Zhangyong Tang, Tianyang Xu, Xuefeng Zhu +6
The development of smart cities has led to the generation of massive amounts of multi-modal data in the context of a range of tasks that enable a comprehensive monitoring of the sm…
GrFormer: A Novel Transformer on Grassmann Manifold for Infrared and Visible Image Fusion
Huan Kang, Hui Li, Xiao-Jun Wu +4
In the field of image fusion, promising progress has been made by modeling data from different modalities as linear subspaces. However, in practice, the source images are often loc…
One Model for ALL: Low-Level Task Interaction Is a Key to Task-Agnostic Image Fusion
Chunyang Cheng, Tianyang Xu, Zhenhua Feng +7
Advanced image fusion methods mostly prioritise high-level missions, where task interaction struggles with semantic gaps, requiring complex bridging mechanisms. In contrast, we pro…
One Latent Space to Rule All Degradations: Unifying Restoration Knowledge for Image Fusion
Haolong Ma, Hui Li, Chunyang Cheng +4
All-in-One Degradation-Aware Fusion Models (ADFMs) as one of multi-modal image fusion models, which aims to address complex scenes by mitigating degradations from source images and…
SMLNet: A SPD Manifold Learning Network for Infrared and Visible Image Fusion
Huan Kang, Hui Li, Tianyang Xu +4
Euclidean representation learning methods have achieved promising results in image fusion tasks, which can be attributed to their clear advantages in handling with linear space. Ho…
MMDRFuse: Distilled Mini-Model with Dynamic Refresh for Multi-Modality Image Fusion
Yanglin Deng, Tianyang Xu, Chunyang Cheng +2
In recent years, Multi-Modality Image Fusion (MMIF) has been applied to many fields, which has attracted many scholars to endeavour to improve the fusion performance. However, the…
Revisiting RGBT Tracking Benchmarks from the Perspective of Modality Validity: A New Benchmark, Problem, and Solution
Zhangyong Tang, Tianyang Xu, Zhenhua Feng +4
RGBT tracking draws increasing attention because its robustness in multi-modal warranting (MMW) scenarios, such as nighttime and adverse weather conditions, where relying on a sing…