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 mapping. To address this issue, this paper proposes an …

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 …

Despite significant advances in continual semantic segmentation (CSS), they still rely on the
pixel-level annotation to train models, which is time-consuming and labor-intensive …

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 …

In recent years, the inherent two-dimensional characteristics of optical images have led to a
plateau in semantic segmentation performance. The complementary nature of Light …

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 …

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

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 …

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