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
GPT-Prompt Controlled Diffusion for Weakly-Supervised Semantic Segmentation
In this process, the existing images and image-level labels provide the necessary control information, where GPT is employed to enrich the prompts, leading to the generation of diverse backgrounds.
Top-K Pooling with Patch Contrastive Learning for Weakly-Supervised Semantic Segmentation
In this paper, we introduce a novel ViT-based WSSS method named top-K pooling with patch contrastive learning (TKP-PCL), which employs a top-K pooling layer to alleviate the limitations of previous max pooling selection.
Dual-Augmented Transformer Network for Weakly Supervised Semantic Segmentation
Weakly supervised semantic segmentation (WSSS), a fundamental computer vision task, which aims to segment out the object within only class-level labels.
COMNet: Co-Occurrent Matching for Weakly Supervised Semantic Segmentation
Image-level weakly supervised semantic segmentation is a challenging task that has been deeply studied in recent years.
Small Objects Matters in Weakly-supervised Semantic Segmentation
Weakly-supervised semantic segmentation (WSSS) performs pixel-wise classification given only image-level labels for training.
From Text to Mask: Localizing Entities Using the Attention of Text-to-Image Diffusion Models
Experiments in various situations demonstrate the advantages of our method compared to strong baselines on this task.
Exploring Limits of Diffusion-Synthetic Training with Weakly Supervised Semantic Segmentation
The advance of generative models for images has inspired various training techniques for image recognition utilizing synthetic images.
CVFC: Attention-Based Cross-View Feature Consistency for Weakly Supervised Semantic Segmentation of Pathology Images
Specifically, CVFC is a three-branch joint framework composed of two Resnet38 and one Resnet50, and the independent branch multi-scale integrated feature map to generate a class activation map (CAM); in each branch, through down-sampling and The expansion method adjusts the size of the CAM; the middle branch projects the feature matrix to the query and key feature spaces, and generates a feature space perception matrix through the connection layer and inner product to adjust and refine the CAM of each branch; finally, through the feature consistency loss and feature cross loss to optimize the parameters of CVFC in co-training mode.
Beyond Discriminative Regions: Saliency Maps as Alternatives to CAMs for Weakly Supervised Semantic Segmentation
Furthermore, we propose random cropping as a stochastic aggregation technique that improves the performance of saliency, making it a strong alternative to CAM for WS3.
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.
