Deep learning (DL) has become one of the mainstream and effective methods for point
cloud analysis tasks such as detection, segmentation and classification. To reduce …

Semantic segmentation, a fundamental task in environmental understanding, aims to assign
each image pixel to a specific class. Despite recent progress, segmentation accuracy in …

Combining RGB images and their corresponding depth maps in semantic segmentation has
proven to be effective in recent years. However, existing RGB-D modal fusion methods …

Transformer can build global relationships between pixels and enhance pixel
representation. The existing methods only establish the context relationship from the whole …

Neural architecture search (NAS) has shown excellent performance. However, existing
semantic segmentation models rely heavily on pre-training on Image-Net or COCO and …

Abstract Monocular Semantic Scene Completion (SSC) empowers intelligent devices to
comprehend voxel occupancy (geometry) and semantics in 3D scenes, attracting significant …

Recently, pixel-to-pixel contrastive learning in single-scale feature space has been widely
studied in semantic segmentation to learn a unified feature expression for pixels of the same …

Few-shot semantic segmentation uses a few annotated data of a specific class in the support
set to segment the target of the same class in the query set. Most existing approaches fail to …

Semi-supervised semantic segmentation needs rich and robust supervision for unlabeled
data. However, promoting or punishing feature similarities with vanilla contrastive learning …

This study introduces a novel method integrating pattern recognition models with causal
inference methodologies to adeptly identify and manage overlapping regions between …