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
Leveraging Swin Transformer for Local-to-Global Weakly Supervised Semantic Segmentation
In recent years, weakly supervised semantic segmentation using image-level labels as supervision has received significant attention in the field of computer vision.
SFC: Shared Feature Calibration in Weakly Supervised Semantic Segmentation
Specifically, we leverage the class prototypes that carry positive shared features and propose a Multi-Scaled Distribution-Weighted (MSDW) consistency loss for narrowing the gap between the CAMs generated through classifier weights and class prototypes during training.
Spatial Structure Constraints for Weakly Supervised Semantic Segmentation
nust-machine-intelligence-laboratory/ssc
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In this paper, we propose spatial structure constraints (SSC) for weakly supervised semantic segmentation to alleviate the unwanted object over-activation of attention expansion.
Question-Answer Cross Language Image Matching for Weakly Supervised Semantic Segmentation
cvi-szu/qa-clims
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Class Activation Map (CAM) has emerged as a popular tool for weakly supervised semantic segmentation (WSSS), allowing the localization of object regions in an image using only image-level labels.
Clustering-Guided Class Activation for Weakly Supervised Semantic Segmentation
DCVL-WSSS/ClusterCAM
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Access 2024
In this paper, we propose a novel class activation scheme that is able to uniformly highlight the whole object region.
PointCT: Point Central Transformer Network for Weakly-supervised Point Cloud Semantic Segmentation
anhthuan1999/PointCT
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Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) 2023
Although point cloud segmentation has a principal role in 3D understanding, annotating fully large-scale scenes for this task can be costly and time-consuming.
Weakly Supervised Semantic Segmentation for Driving Scenes
k0u-id/carb
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Notably, the proposed method achieves 51. 8\% mIoU on the Cityscapes test dataset, showcasing its potential as a strong WSSS baseline on driving scene datasets.
TagCLIP: A Local-to-Global Framework to Enhance Open-Vocabulary Multi-Label Classification of CLIP Without Training
As a result, we dissect the preservation of patch-wise spatial information in CLIP and proposed a local-to-global framework to obtain image tags.
Progressive Feature Self-reinforcement for Weakly Supervised Semantic Segmentation
jessie459/feature-self-reinforcement
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Building upon this, we introduce a complementary self-enhancement method that constrains the semantic consistency between these confident regions and an augmented image with the same class labels.
Foundation Model Assisted Weakly Supervised Semantic Segmentation
HAL-42/FMA-WSSS
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This work aims to leverage pre-trained foundation models, such as contrastive language-image pre-training (CLIP) and segment anything model (SAM), to address weakly supervised semantic segmentation (WSSS) using image-level labels.
