Semantic segmentation achieves fine-grained scene parsing in any scenario, making it one
of the key research directions to facilitate the development of human visual attention …

The Area Under the ROC Curve (AUC) is a well-known metric for evaluating instance-level
long-tail learning problems. In the past two decades, many AUC optimization methods have …

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

Recently, semantic segmentation of remote sensing images has witnessed rapid
advancement with the adoption of deep neural networks. Contextual cues, referring to the …

Few-shot segmentation aims to train a segmentation model that can quickly adapt to novel
classes referring to only a few annotated samples. Existing few-shot segmentation methods …

Lane segmentation at night is a challenging problem in autonomous driving perception,
which is beneficial to improve the robustness of the application. Existing methods has shown …

Semantic Segmentation in side-scan sonar images (SSS-Seg) is an emerging topic and
plays important function in sonar image interpretation. However, due to the interference of …

In the task of semantic understanding of remote sensing images, most current research
focuses on learning contextual information through attention mechanisms or multiple …

Recent advances in garment-centric image generation from text and image prompts based
on diffusion models are impressive. However, existing methods lack support for various …

In semantic segmentation, the efficient representation of multi-scale context is of paramount
importance. Inspired by the remarkable performance of Vision Transformers (ViT) in image …