Semantic image segmentation, which becomes one of the key applications in image
processing and computer vision domain, has been used in multiple domains such as …

Semantic segmentation is the pixel-wise labeling of an image. Boosted by the extraordinary
ability of convolutional neural networks (CNN) in creating semantic, high-level and …

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

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 …

Semantic classes can be either things (objects with a well-defined shape, eg car, person) or
stuff (amorphous background regions, eg grass, sky). While lots of classification and …

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

Visual understanding of complex urban street scenes is an enabling factor for a wide range
of applications. Object detection has benefited enormously from large-scale datasets …

Most weakly supervised semantic segmentation (WSSS) methods follow the pipeline that
generates pseudo-masks initially and trains the segmentation model with the pseudo-masks …

Semantic labelling and instance segmentation are two tasks that require particularly costly
annotations. Starting from weak supervision in the form of bounding box detection …

We investigate and improve self-supervision as a drop-in replacement for ImageNet
pretraining, focusing on automatic colorization as the proxy task. Self-supervised training …