This paper proposes a coupled learning framework to break the performance bottleneck of
infrared-visible image fusion and segmentation called MRFS. By leveraging the intrinsic …

For image fusion tasks, it is inefficient for the diffusion model to iterate multiple times on the
original resolution image for feature map**. To address this issue, this paper proposes an …

Segmentation of road negative obstacles (ie, potholes and cracks) is important to the safety
of autonomous driving. Although existing RGB-D fusion networks could achieve acceptable …

Semantic segmentation based on 4D light field (LF) images exhibits superior performance
by exploiting rich spatial and angular information. However, current methods only focus on …

The fusion of RGB and thermal data for dense prediction tasks has been demonstrated to be
an effective and robust approach in autonomous driving. Nevertheless, the challenge lies in …

The primary objective of infrared and visible image fusion is to amalgamate information from
multi-modal images into fused images containing salient targets and abundant details …

Infrared and visible image fusion aims to generate a comprehensive image that retains both
salient targets of the infrared image and texture details of the visible image. However …

Image-level weakly supervised semantic segmentation (WSSS) has received substantial
attention due to its cost-effective annotation process. In WSSS, Class Activation Maps …

The inherent imaging properties of sensors result in two distinct differences between the
data from the two modalities in RGB-T Salient Object Detection (SOD) tasks. Namely …

RGB-Thermal semantic segmentation is important in widespread applications in adverse
illumination conditions, such as autonomous driving and robotic sensing. However, most …