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

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

Weakly supervised semantic segmentation has witnessed great achievements with image-
level labels. Several recent approaches use the CLIP model to generate pseudo labels for …

With the goal to detect both the object categories appearing in the training phase and those
never have been observed before testing, zero-shot object detection (ZSD) becomes a …

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

Abstract Recently One-stage Weakly Supervised Semantic Segmentation (WSSS) with
image-level labels has gained increasing interest due to simplification over its cumbersome …

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 …

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

In recent years, there has been remarkable progress in weakly supervised semantic
segmentation (WSSS), with vision transformer (ViT) architectures emerging as a natural fit …