Recent progress in semantic image segmentation
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 …
processing and computer vision domain, has been used in multiple domains such as …
A survey on deep learning-based architectures for semantic segmentation on 2d images
I Ulku, E Akagündüz - Applied Artificial Intelligence, 2022 - Taylor & Francis
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 …
ability of convolutional neural networks (CNN) in creating semantic, high-level and …
Unsupervised semantic segmentation by contrasting object mask proposals
W Van Gansbeke, S Vandenhende… - Proceedings of the …, 2021 - openaccess.thecvf.com
Being able to learn dense semantic representations of images without supervision is an
important problem in computer vision. However, despite its significance, this problem …
important problem in computer vision. However, despite its significance, this problem …
Weakly-supervised semantic segmentation network with deep seeded region growing
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 …
using image-level labels as supervision, which is important since it can significantly reduce …
Coco-stuff: Thing and stuff classes in context
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 …
stuff (amorphous background regions, eg grass, sky). While lots of classification and …
Object region mining with adversarial erasing: A simple classification to semantic segmentation approach
We investigate a principle way to progressively mine discriminative object regions using
classification networks to address the weakly-supervised semantic segmentation problems …
classification networks to address the weakly-supervised semantic segmentation problems …
The cityscapes dataset for semantic urban scene understanding
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 …
of applications. Object detection has benefited enormously from large-scale datasets …
Pseudo-mask matters in weakly-supervised semantic segmentation
Most weakly supervised semantic segmentation (WSSS) methods follow the pipeline that
generates pseudo-masks initially and trains the segmentation model with the pseudo-masks …
generates pseudo-masks initially and trains the segmentation model with the pseudo-masks …
Simple does it: Weakly supervised instance and semantic segmentation
Semantic labelling and instance segmentation are two tasks that require particularly costly
annotations. Starting from weak supervision in the form of bounding box detection …
annotations. Starting from weak supervision in the form of bounding box detection …
Colorization as a proxy task for visual understanding
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 …
pretraining, focusing on automatic colorization as the proxy task. Self-supervised training …