[HTML][HTML] A dual-branch weakly supervised learning based network for accurate mapping of woody vegetation from remote sensing images
Mapping 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 …
A survey on label-efficient deep image segmentation: Bridging the gap between weak supervision and dense prediction
The rapid development of deep learning has made a great progress in image segmentation,
one of the fundamental tasks of computer vision. However, the current segmentation …
one of the fundamental tasks of computer vision. However, the current segmentation …
Segnext: Rethinking convolutional attention design for semantic segmentation
We present SegNeXt, a simple convolutional network architecture for semantic
segmentation. Recent transformer-based models have dominated the field of se-mantic …
segmentation. Recent transformer-based models have dominated the field of se-mantic …
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 …
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 …
Extracting class activation maps from non-discriminative features as well
Extracting class activation maps (CAM) from a classification model often results in poor
coverage on foreground objects, ie, only the discriminative region (eg, the" head" of" sheep") …
coverage on foreground objects, ie, only the discriminative region (eg, the" head" of" sheep") …
Boundary-enhanced co-training for weakly supervised semantic segmentation
The existing weakly supervised semantic segmentation (WSSS) methods pay much
attention to generating accurate and complete class activation maps (CAMs) as pseudo …
attention to generating accurate and complete class activation maps (CAMs) as pseudo …
Learning multi-modal class-specific tokens for weakly supervised dense object localization
Weakly supervised dense object localization (WSDOL) relies generally on Class Activation
Mapping (CAM), which exploits the correlation between the class weights of the image …
Mapping (CAM), which exploits the correlation between the class weights of the image …
Mars: model-agnostic biased object removal without additional supervision for weakly-supervised semantic segmentation
Weakly-supervised semantic segmentation aims to reduce labeling costs by training
semantic segmentation models using weak supervision, such as image-level class labels …
semantic segmentation models using weak supervision, such as image-level class labels …
Out-of-candidate rectification for weakly supervised semantic segmentation
Weakly supervised semantic segmentation is typically inspired by class activation maps,
which serve as pseudo masks with class-discriminative regions highlighted. Although …
which serve as pseudo masks with class-discriminative regions highlighted. Although …