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 …

The class activation maps are generated from the final convolutional layer of CNN. They can
highlight discriminative object regions for the class of interest. These discovered object …

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 …

Existing studies in weakly-supervised semantic segmentation (WSSS) using image-level
weak supervision have several limitations: sparse object coverage, inaccurate object …

Image-level weakly supervised semantic segmentation is a challenging problem that has
been deeply studied in recent years. Most of advanced solutions exploit class activation map …

Semantic segmentation aims to classify every pixel of an input image. Considering the
difficulty of acquiring dense labels, researchers have recently been resorting to weak labels …

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 …

This paper studies the problem of learning semantic segmentation from image-level
supervision only. Current popular solutions leverage object localization maps from …

The main obstacle to weakly supervised semantic image segmentation is the difficulty of
obtaining pixel-level information from coarse image-level annotations. Most methods based …

Saliency detection on RGB-D images is receiving more and more research interests
recently. Previous models adopt the early fusion or the result fusion scheme to fuse the input …