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Weakly-Supervised Semantic Segmentation

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 )

Papers

Showing 3140 of 296 papers

TitleStatusHype
Foundation Model Assisted Weakly Supervised Semantic SegmentationCode1
Weakly Supervised Semantic Segmentation by Knowledge Graph InferenceCode1
Background Activation Suppression for Weakly Supervised Object Localization and Semantic SegmentationCode1
MCTformer+: Multi-Class Token Transformer for Weakly Supervised Semantic SegmentationCode1
Hierarchical Semantic Contrast for Weakly Supervised Semantic SegmentationCode1
Prompting classes: Exploring the Power of Prompt Class Learning in Weakly Supervised Semantic SegmentationCode1
AME-CAM: Attentive Multiple-Exit CAM for Weakly Supervised Segmentation on MRI Brain TumorCode1
Conditional Diffusion Models for Weakly Supervised Medical Image SegmentationCode1
Segment Anything Model (SAM) Enhanced Pseudo Labels for Weakly Supervised Semantic SegmentationCode1
An Alternative to WSSS? An Empirical Study of the Segment Anything Model (SAM) on Weakly-Supervised Semantic Segmentation ProblemsCode1
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