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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 4150 of 296 papers

TitleStatusHype
Context Decoupling Augmentation for Weakly Supervised Semantic SegmentationCode1
GETAM: Gradient-weighted Element-wise Transformer Attention Map for Weakly-supervised Semantic segmentationCode1
Adaptive Early-Learning Correction for Segmentation from Noisy AnnotationsCode1
DHR: Dual Features-Driven Hierarchical Rebalancing in Inter- and Intra-Class Regions for Weakly-Supervised Semantic SegmentationCode1
Beyond Semantic to Instance Segmentation: Weakly-Supervised Instance Segmentation via Semantic Knowledge Transfer and Self-RefinementCode1
Class-incremental Continual Learning for Instance Segmentation with Image-level Weak SupervisionCode1
An Alternative to WSSS? An Empirical Study of the Segment Anything Model (SAM) on Weakly-Supervised Semantic Segmentation ProblemsCode1
Class Tokens Infusion for Weakly Supervised Semantic SegmentationCode1
Dual Progressive Transformations for Weakly Supervised Semantic SegmentationCode1
Complementary Patch for Weakly Supervised Semantic SegmentationCode1
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