Weakly-Supervised Semantic Segmentation
145 papers with code • 9 benchmarks • 8 datasets
The semantic segmentation task is to assign a label from a label set to each pixel in an image. In the case of fully supervised setting, the dataset consists of images and their corresponding pixel-level class-specific annotations (expensive pixel-level annotations). However, in the weakly-supervised setting, the dataset consists of images and corresponding annotations that are relatively easy to obtain, such as tags/labels of objects present in the image.
( Image credit: Weakly-Supervised Semantic Segmentation Network with Deep Seeded Region Growing )
Benchmarks
These leaderboards are used to track progress in Weakly-Supervised Semantic Segmentation
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Libraries
Use these libraries to find Weakly-Supervised Semantic Segmentation models and implementationsDatasets
Cityscapes
ADE20K
PASCAL VOC 2012 test
ScribbleSup
ScribbleKITTI
ACDC Scribbles
CheXlocalize
Latest papers with no code
Branches Mutual Promotion for End-to-End Weakly Supervised Semantic Segmentation
Existing methods adopt an online-trained classification branch to provide pseudo annotations for supervising the segmentation branch.
Rethinking Class Activation Maps for Segmentation: Revealing Semantic Information in Shallow Layers by Reducing Noise
A major limitation to the performance of the class activation maps is the small spatial resolution of the feature maps in the last layer of the convolutional neural network.
CG-fusion CAM: Online segmentation of laser-induced damage on large-aperture optics
Online segmentation of laser-induced damage on large-aperture optics in high-power laser facilities is challenged by complicated damage morphology, uneven illumination and stray light interference.
P-NOC: adversarial training of CAM generating networks for robust weakly supervised semantic segmentation priors
Weakly Supervised Semantic Segmentation (WSSS) techniques explore individual regularization strategies to refine Class Activation Maps (CAMs).
Mitigating Undisciplined Over-Smoothing in Transformer for Weakly Supervised Semantic Segmentation
A surge of interest has emerged in weakly supervised semantic segmentation due to its remarkable efficiency in recent years.
Segment Anything is A Good Pseudo-label Generator for Weakly Supervised Semantic Segmentation
Weakly supervised semantic segmentation with weak labels is a long-lived ill-posed problem.
Removing supervision in semantic segmentation with local-global matching and area balancing
Our model attains state-of-the-art in Weakly Supervised Semantic Segmentation, only image-level labels, with 75% mIoU on PascalVOC2012 val set and 46% on MS-COCO2014 val set.
USAGE: A Unified Seed Area Generation Paradigm for Weakly Supervised Semantic Segmentation
Seed area generation is usually the starting point of weakly supervised semantic segmentation (WSSS).
Exploit CAM by itself: Complementary Learning System for Weakly Supervised Semantic Segmentation
Motivated by this simple but effective learning pattern, we propose a General-Specific Learning Mechanism (GSLM) to explicitly drive a coarse-grained CAM to a fine-grained pseudo mask.
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 classifier and the pixel-level features.
