Few Annotated Pixels and Point Cloud Based Weakly Supervised Semantic Segmentation of Driving Scenes Background and Research Issues Semantic segmentation, a critical task in computer vision, has extensive applications in domains like autonomous driving. However, traditional fully-supervised semantic segmentation methods require exhaustive pixel-lev...

A New Perspective on Domain Adaptive Semantic Segmentation: T2S-DA Study Background and Significance Semantic segmentation plays a crucial role in computer vision, but its performance often relies on extensive labeled data. However, acquiring labeled data is costly, especially in complex scenarios. To address this, many studies turn to synthetic da...

Semantic segmentation aims to predict category labels for each pixel in an image (Liu et al., 2021; Wang et al., 2021) and is widely used in scene understanding, medical image analysis, autonomous driving, geographic information systems, and augmented reality (Strudel et al., 2021; Sun et al., 2023). Although the development of deep neural networks...

Stacked Deconvolutional Network for Semantic Segmentation Introduction Semantic segmentation is a critical task in the field of computer vision, aiming to classify each pixel in an image and predict its category. However, existing Fully Convolutional Networks (FCNs) have limitations in handling spatial resolution, often leading to problems such as ...

Weakly Supervised Semantic Image Segmentation via Alternate Self-Dual Teaching Background Introduction With the continuous development of the computer vision field, semantic segmentation has become an important and active research direction. Traditional semantic segmentation methods rely on manually labeled pixel-level tags; however, obtaining thes...