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 )
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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
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
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
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
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
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
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
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.