This work presents a new method for unsupervised thermal image classification and
semantic segmentation by transferring knowledge from the RGB domain using a multi …

We present a new method to adapt an RGB-trained water segmentation network to target-
domain aerial thermal imagery using online self-supervision by leveraging texture and …

Mirror segmentation is an emerging computer vision task that is extensively applied in
various fields. However, it presents significant challenges to existing segmentation methods …

The acquisition of global context and boundary information is crucial for the semantic
segmentation of remote sensing (RS) images. In contrast to convolutional neural networks …

This paper proposes a smoke semantic segmentation framework EANet based on boundary
enhancement and attention mechanism. It integrates semantic segmentation and semantic …

Video semantic segmentation has achieved great success, which is significant for road
scene understanding. However, semantic segmentation remains challenging in poor …

Colorizing unpaired thermal infrared images is a challenging task that existing methods
struggle to perform effectively, often resulting in blurry details and unclear textures. To …

Current semantic segmentation methods have high accuracy. However, it has the
disadvantage of high computational complexity and time consumption, which makes it …

Stable imaging in adverse environments (eg, total darkness) makes thermal infrared (TIR)
cameras a prevalent option for night scene perception. However, the low contrast and lack of …

Thermal infrared image colorization is very difficult, and colorized images suffer from poor
texture detail recovery and low color matching. To solve the above problems, this paper …