Convolutional neural network (CNN) has achieved great success in hyperspectral image
(HSI) classification. However, the local receptive field of CNNs leads to the drawback in …

In the dynamic landscape of saliency detection, convolutional neural networks have
emerged as catalysts for innovation, but remain largely tailored for RGB imagery, falling …

Advancements in the rapidly evolving specialization of deep learning have aided in
improving several natural language understanding tasks. Sentiment and emotion …

The generalization capability of unsupervised domain adaptation can mitigate the need for
extensive pixel-level annotations to train semantic segmentation networks by training …

In this paper, we introduce a two-stage deep learning-based image restoration network and
its application to remove shadow information from a single image, named by ESCNet …

Semantic segmentation using RGB (Red-Green-Blue) images and thermal datas is an
indispensable component of autonomous driving. The key to RGB-Thermal (RGB and …

Human motion prediction is attracting increasing attention for its numerous potential
applications in fields including autonomous driving, video surveillance and virtual reality …

The image super-resolution (SR) operation holds multiple solutions with the one-to-many
map** from low-resolution (LR) to high-resolution (HR) space. However, the SR of …

Large-scale point cloud semantic segmentation presents a crucial yet challenging task.
Current point cloud analysis approaches typically partition data into volumetric blocks …

The primary effects of haze on captured images/frames are visibility degradation and color
disturbance. Even though extensive research has been done on the tasks of video …