The task of semantic segmentation holds a fundamental position in the field of computer
vision. Assigning a semantic label to each pixel in an image is a challenging task. In recent …

The annotations used during the training process are crucial for the inference results of
remote sensing images (RSIs) based on a deep learning framework. Unlabeled RSIs can be …

Despite remarkable progress, weakly supervised segmentation methods are still inferior to
their fully supervised counterparts. We obverse that the performance gap mainly comes from …

Semantic segmentation is a challenging task in computer vision systems. A lot of methods
have been developed to tackle this problem ranging from autonomous vehicles, human …

Abstract Weakly Supervised Object Detection (WSOD) has emerged as an effective tool to
train object detectors using only the image-level category labels. However, without object …

Nowadays, deep learning has achieved remarkable results in many computer vision related
tasks, among which the support of big data is essential. In this paper, we propose a full stage …

Abstract The Convolutional Neural Network (CNN) based region proposal generation
method (ie region proposal network), trained using bounding box annotations, is an …

Training a deep network to perform semantic segmentation requires large amounts of
labeled data. To alleviate the manual effort of annotating real images, researchers have …

Understanding the surrounding environment of the vehicle is still one of the challenges for
autonomous driving. This paper addresses 360-degree road scene semantic segmentation …

Surgical tool localization is an essential task for the automatic analysis of endoscopic
videos. In the literature, existing methods for tool localization, tracking and segmentation …