Abstract Weakly-Supervised Semantic Segmentation (WSSS) using image-level labels
typically utilizes Class Activation Map (CAM) to generate the pseudo labels. Limited by the …

Accurate segmentation in histopathology images at pixel-level plays a critical role in the
digital pathology workflow. The development of weakly supervised methods for …

Weakly supervised semantic segmentation (WSSS) with image-level labels aims to achieve
segmentation tasks without dense annotations. However attributed to the frequent coupling …

Great progress has been witnessed for weakly supervised semantic segmentation, which
aims to segment objects without dense pixel annotations. Most approaches concentrate on …

Weakly supervised object localization and semantic segmentation aim to localize objects
using only image-level labels. Recently, a new paradigm has emerged by generating a …

Segmenting the semantic regions of point clouds is a crucial step for intelligent agents to
understand 3D scenes. Weakly supervised point cloud segmentation is highly desirable …

Weakly supervised semantic segmentation (WSSS) is a challenging yet important research
field in vision community. In WSSS, the key problem is to generate high-quality pseudo …

Few shot semantic segmentation has been proposed to enhance the generalization ability of
traditional models with limited data. Previous works mainly focus on the supervised tasks …

Accurate tissue segmentation in histopathological images is essential for promoting the
development of precision pathology. However, the size of the digital pathological image is …

Remote sensing image scene classification methods based on deep learning have been
widely studied and discussed. However, most of the network architectures are directly reliant …