Medical vision-language models (VLMs) combine computer vision (CV) and natural
language processing (NLP) to analyze visual and textual medical data. Our paper reviews …

Artificial intelligence (AI) approaches nowadays have gained remarkable success in single-
modality-dominated remote sensing (RS) applications, especially with an emphasis on …

Learning-based infrared small object detection methods currently rely heavily on the
classification backbone network. This tends to result in tiny object loss and feature …

Depth information has proven to be a useful cue in the semantic segmentation of RGB-D
images for providing a geometric counterpart to the RGB representation. Most existing works …

Semantic segmentation of remote sensing images plays an important role in a wide range of
applications, including land resource management, biosphere monitoring, and urban …

Accurate and automatic segmentation of medical images is a crucial step for clinical
diagnosis and analysis. The convolutional neural network (CNN) approaches based on the …

Contextual information has been shown to be powerful for semantic segmentation. This work
proposes a novel Context-based Tandem Network (CTNet) by interactively exploring the …

As a prerequisite for autonomous driving, scene understanding has attracted extensive
research. With the rise of the convolutional neural network (CNN)-based deep learning …

Translating satellite imagery into maps requires intensive effort and time, especially leading
to inaccurate maps of the affected regions during disaster and conflict. The combination of …

Unsupervised domain adaption (UDA), which aims to enhance the segmentation
performance of deep models on unlabeled data, has recently drawn much attention. In this …