Unsupervised semantic segmentation aims to discover and localize semantically meaningful
categories within image corpora without any form of annotation. To solve this task …

Though image-level weakly supervised semantic segmentation (WSSS) has achieved great
progress with Class Activation Maps (CAMs) as the cornerstone, the large supervision gap …

Weakly supervised semantic segmentation task aims to learn a segmentation model with
only image-level annotations. Existing methods generally refine the initial seeds to obtain …

Image segmentation is a crucial task in computer vision, which divides a digital image into
multiple segments and objects. In agriculture, image segmentation is extensively used for …

Construction sites are incorporating cameras to gather imagery data for project
management. While transformer-based deep models show promise in recognizing …

This paper discusses shadow detection problem, and proposes a light-weight network to
achieve both accurate detection results and high computation efficiency. Firstly, we begin by …

The transformer-based semantic segmentation approaches, which divide the image into
different regions by sliding windows and model the relation inside each window, have …

Objective. Whole-body positron emission tomography/computed tomography (PET/CT)
scans are an important tool for diagnosing various malignancies (eg malignant melanoma …

The use of social media has made it easy to communicate and share information over the
internet. However, it also brings issues such as data privacy leakage, which can be …

Weakly Supervised Semantic Segmentation (WSSS) is a challenging task aiming to learn
the segmentation labels from class-level labels. In the literature, exploiting the information …