Review the state-of-the-art technologies of semantic segmentation based on deep learning
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 …
information and predict the semantic category of each pixel from a given label set. With the …
[HTML][HTML] A dual-branch weakly supervised learning based network for accurate map** of woody vegetation from remote sensing images
Map** woody vegetation from aerial images is an important task bluein environment
monitoring and management. A few studies have shown that semantic segmentation …
monitoring and management. A few studies have shown that semantic segmentation …
Token contrast for weakly-supervised semantic segmentation
Abstract Weakly-Supervised Semantic Segmentation (WSSS) using image-level labels
typically utilizes Class Activation Map (CAM) to generate the pseudo labels. Limited by the …
typically utilizes Class Activation Map (CAM) to generate the pseudo labels. Limited by the …
Clip is also an efficient segmenter: A text-driven approach for weakly supervised semantic segmentation
Weakly supervised semantic segmentation (WSSS) with image-level labels is a challenging
task. Mainstream approaches follow a multi-stage framework and suffer from high training …
task. Mainstream approaches follow a multi-stage framework and suffer from high training …
Regional semantic contrast and aggregation for weakly supervised semantic segmentation
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 …
challenging since it is hard to infer dense object regions from sparse semantic tags. Despite …
Self-supervised image-specific prototype exploration for weakly supervised semantic segmentation
Abstract Weakly Supervised Semantic Segmentation (WSSS) based on image-level labels
has attracted much attention due to low annotation costs. Existing methods often rely on …
has attracted much attention due to low annotation costs. Existing methods often rely on …
L2g: A simple local-to-global knowledge transfer framework for weakly supervised semantic segmentation
Mining precise class-aware attention maps, aka, class activation maps, is essential for
weakly supervised semantic segmentation. In this paper, we present L2G, a simple online …
weakly supervised semantic segmentation. In this paper, we present L2G, a simple online …
A mutually supervised graph attention network for few-shot segmentation: The perspective of fully utilizing limited samples
Fully supervised semantic segmentation has performed well in many computer vision tasks.
However, it is time-consuming because training a model requires a large number of pixel …
However, it is time-consuming because training a model requires a large number of pixel …
Abdomenct-1k: Is abdominal organ segmentation a solved problem?
With the unprecedented developments in deep learning, automatic segmentation of main
abdominal organs seems to be a solved problem as state-of-the-art (SOTA) methods have …
abdominal organs seems to be a solved problem as state-of-the-art (SOTA) methods have …
Clims: Cross language image matching for weakly supervised semantic segmentation
It has been widely known that CAM (Class Activation Map) usually only activates
discriminative object regions and falsely includes lots of object-related backgrounds. As only …
discriminative object regions and falsely includes lots of object-related backgrounds. As only …