The goal of semantic segmentation is to segment the input image according to semantic
information and predict the semantic category of each pixel from a given label set. With the …

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 …

Abstract 3D Semantic Scene Completion (SSC) has emerged as a nascent and pivotal
undertaking in autonomous driving aiming to predict the voxel occupancy within volumetric …

Weakly supervised semantic segmentation produces a pixel-level localization from class
labels; but a classifier trained on such labels is likely to restrict its focus to a small …

Semantic segmentation has made tremendous progress in recent years. However, satisfying
performance highly depends on a large number of pixel-level annotations. Therefore, in this …

Being able to learn dense semantic representations of images without supervision is an
important problem in computer vision. However, despite its significance, this problem …

Weakly supervised semantic segmentation produces pixel-level localization from class
labels; however, a classifier trained on such labels is likely to focus on a small discriminative …

Weakly supervised segmentation methods using bounding box annotations focus on
obtaining a pixel-level mask from each box containing an object. Existing methods typically …

Weakly supervised semantic segmentation (WSSS) methods are often built on pixel-level
localization maps obtained from a classifier. However, training on class labels only …

Semantic segmentation is a challenging task in the absence of densely labelled data. Only
relying on class activation maps (CAM) with image-level labels provides deficient …