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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

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Showing 26–50 of 296 papers

TitleStatusHype
Class Tokens Infusion for Weakly Supervised Semantic SegmentationCode1
PointCT: Point Central Transformer Network for Weakly-supervised Point Cloud Semantic SegmentationCode1
Weakly Supervised Semantic Segmentation for Driving ScenesCode1
TagCLIP: A Local-to-Global Framework to Enhance Open-Vocabulary Multi-Label Classification of CLIP Without TrainingCode1
Progressive Feature Self-reinforcement for Weakly Supervised Semantic SegmentationCode1
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
MARS: Model-agnostic Biased Object Removal without Additional Supervision for Weakly-Supervised Semantic SegmentationCode1
WeakTr: Exploring Plain Vision Transformer for Weakly-supervised Semantic SegmentationCode1
Extracting Class Activation Maps from Non-Discriminative Features as wellCode1
Token Contrast for Weakly-Supervised Semantic SegmentationCode1
Self Correspondence Distillation for End-to-End Weakly-Supervised Semantic SegmentationCode1
Class-incremental Continual Learning for Instance Segmentation with Image-level Weak SupervisionCode1
FPR: False Positive Rectification for Weakly Supervised Semantic SegmentationCode1
Boundary-Enhanced Co-Training for Weakly Supervised Semantic SegmentationCode1
Weakly Supervised Semantic Segmentation via Adversarial Learning of Classifier and ReconstructorCode1
Weakly Supervised Semantic Segmentation for Large-Scale Point CloudCode1
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