Weakly-Supervised Semantic Segmentation
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Benchmarks
PASCAL VOC 2012 val
PASCAL VOC 2012 test
COCO 2014 val
PASCAL VOC 2012 train
ADE20K val
COCO-Stuff val
Cityscapes test
Cityscapes val
PASCAL Context val
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Most implemented
Weakly Supervised Semantic Segmentation for Large-Scale Point Cloud
Puzzle-CAM: Improved localization via matching partial and full features
Constrained-CNN losses for weakly supervised segmentation
Papers
Rewis3d: Reconstruction Improves Weakly-Supervised Semantic Segmentation
We present Rewis3d, a framework that leverages recent advances in feed-forward 3D reconstruction to significantly improve weakly supervised semantic segmentation on 2D images. Obtaining dense, pixel-level annotations rem…
Weakly-Supervised Semantic Segmentation3D ReconstructionPoint CloudsTWLR: Text-Guided Weakly-Supervised Lesion Localization and Severity Regression for Explainable Diabetic Retinopathy Grading
Accurate medical image analysis can greatly assist clinical diagnosis, but its effectiveness relies on high-quality expert annotations Obtaining pixel-level labels for medical images, particularly fundus images, remains …
Weakly-Supervised Semantic SegmentationDiabetic Retinopathy GradingLesion SegmentationDiffusion-Guided Knowledge Distillation for Weakly-Supervised Low-Light Semantic Segmentation
Weakly-supervised semantic segmentation aims to assign category labels to each pixel using weak annotations, significantly reducing manual annotation costs. Although existing methods have achieved remarkable progress in …
Weakly-Supervised Semantic SegmentationKnowledge DistillationSPARS: Self-Play Adversarial Reinforcement Learning for Segmentation of Liver Tumours
Accurate tumour segmentation is vital for various targeted diagnostic and therapeutic procedures for cancer, e.g., planning biopsies or tumour ablations. Manual delineation is extremely labour-intensive, requiring substa…
DiagnosticSemantic SegmentationWeakly supervised Semantic SegmentationWeakly-Supervised Semantic SegmentationExploring CLIP's Dense Knowledge for Weakly Supervised Semantic Segmentation
Weakly Supervised Semantic Segmentation (WSSS) with image-level labels aims to achieve pixel-level predictions using Class Activation Maps (CAMs). Recently, Contrastive Language-Image Pre-training (CLIP) has been introdu…
AttributeSemantic SegmentationWeakly supervised Semantic SegmentationWeakly-Supervised Semantic SegmentationExploiting Inherent Class Label: Towards Robust Scribble Supervised Semantic Segmentation
Scribble-based weakly supervised semantic segmentation leverages only a few annotated pixels as labels to train a segmentation model, presenting significant potential for reducing the human labor involved in the annotati…
SegmentationSemantic SegmentationWeakly supervised Semantic SegmentationWeakly-Supervised Semantic Segmentation