Weakly-Supervised Semantic Segmentation
144 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
Background Noise Reduction of Attention Map for Weakly Supervised Semantic Segmentation
In weakly-supervised semantic segmentation (WSSS) using only image-level class labels, a problem with CNN-based Class Activation Maps (CAM) is that they tend to activate the most discriminative local regions of objects.
Rethinking Saliency-Guided Weakly-Supervised Semantic Segmentation
This paper presents a fresh perspective on the role of saliency maps in weakly-supervised semantic segmentation (WSSS) and offers new insights and research directions based on our empirical findings.
Auxiliary Tasks Enhanced Dual-affinity Learning for Weakly Supervised Semantic Segmentation
We propose AuxSegNet+, a weakly supervised auxiliary learning framework to explore the rich information from these saliency maps and the significant inter-task correlation between saliency detection and semantic segmentation.
HistoSegCap: Capsules for Weakly-Supervised Semantic Segmentation of Histological Tissue Type in Whole Slide Images
Digital pathology involves converting physical tissue slides into high-resolution Whole Slide Images (WSIs), which pathologists analyze for disease-affected tissues.
CoBra: Complementary Branch Fusing Class and Semantic Knowledge for Robust Weakly Supervised Semantic Segmentation
This includes not only the masks generated by our model, but also the segmentation results derived from utilizing these masks as pseudo labels.
SemPLeS: Semantic Prompt Learning for Weakly-Supervised Semantic Segmentation
In this way, SemPLeS can perform better semantic alignment between object regions and the associated class labels, resulting in desired pseudo masks for training the segmentation model.
Weakly-Supervised Semantic Segmentation of Circular-Scan, Synthetic-Aperture-Sonar Imagery
We propose a weakly-supervised framework for the semantic segmentation of circular-scan synthetic-aperture-sonar (CSAS) imagery.
P2Seg: Pointly-supervised Segmentation via Mutual Distillation
Nevertheless, weakly supervised semantic segmentation methods are proficient in utilizing intra-class feature consistency to capture the boundary contours of the same semantic regions.
Densify Your Labels: Unsupervised Clustering with Bipartite Matching for Weakly Supervised Point Cloud Segmentation
We propose a weakly supervised semantic segmentation method for point clouds that predicts "per-point" labels from just "whole-scene" annotations while achieving the performance of recent fully supervised approaches.
Weakly-Supervised Semantic Segmentation with Image-Level Labels: from Traditional Models to Foundation Models
In this paper, we focus on the WSSS with image-level labels, which is the most challenging form of WSSS.
