During the long history of computer vision, one of the grand challenges has been semantic
segmentation which is the ability to segment an unknown image into different parts and …

Image semantic segmentation is one of the most important tasks in the field of computer
vision, and it has made great progress in many applications. Many fully supervised deep …

Learning semantic segmentation from weakly-labeled (eg, image tags only) data is
challenging since it is hard to infer dense object regions from sparse semantic tags. Despite …

We present a causal inference framework to improve Weakly-Supervised Semantic
Segmentation (WSSS). Specifically, we aim to generate better pixel-level pseudo-masks by …

Deep-learning methods have recently started being employed for addressing surface-defect
detection problems in industrial quality control. However, with a large amount of data …

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

This paper studies the problem of learning image semantic segmentation networks only
using image-level labels as supervision, which is important since it can significantly reduce …

Weakly supervised learning with only coarse labels can obtain visual explanations of deep
neural network such as attention maps by back-propagating gradients. These attention …

We investigate a principle way to progressively mine discriminative object regions using
classification networks to address the weakly-supervised semantic segmentation problems …

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 …