Abstract RGB-Thermal infrared (RGB-T) image analysis has been actively studied in recent
years. In the past decade, it has received wide attention and made a lot of important …

This research focuses on the discovery and localization of hidden objects in the wild and
serves unmanned systems. Through empirical analysis, infrared and visible image fusion …

In recent years, various neural network architectures for computer vision have been devised,
such as the visual transformer and multilayer perceptron (MLP). A transformer based on an …

Abstract Deep Learning-based Unsupervised Salient Object Detection (USOD) mainly relies
on the noisy saliency pseudo labels that have been generated from traditional handcraft …

Mirror segmentation, an emerging discipline in the field of computer vision, involves the
identification and marking of mirrors in an image. Current mirror segmentation methods rely …

Light field salient object detection (LF SOD) has recently received increasing attention.
However, most current works typically rely on an individual focal stack backbone for feature …

Motivated by the pursuit of efficient salient object detection in remote sensing, researchers
have devoted considerable efforts to devising lightweight models due to low running …

Image modality is not perfect as it often fails in certain conditions, eg, night and fast motion.
This significantly limits the robustness and versatility of existing multi-modal (ie, Image+ X) …

Multimodal transmission-line detection (TLD) and other vision-related tasks in smart grids
have garnered increasing attention due to advances in deep-learning technologies and …

Fusing an arbitrary number of modalities is vital for achieving robust multi-modal fusion of
semantic segmentation yet remains less explored to date. Recent endeavors regard RGB …