Papers Weakly-Supervised Semantic Segmentation
“Weakly-Supervised Semantic Segmentation” 태그가 달린 논문 299편 · 필터 해제
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 SegmentationDOEI: Dual Optimization of Embedding Information for Attention-Enhanced Class Activation Maps
Weakly supervised semantic segmentation (WSSS) typically utilizes limited semantic annotations to obtain initial Class Activation Maps (CAMs). However, due to the inadequate coupling between class activation responses an…
Semantic SegmentationWeakly supervised Semantic SegmentationWeakly-Supervised Semantic SegmentationSuperpixel Boundary Correction for Weakly-Supervised Semantic Segmentation on Histopathology Images
With the rapid advancement of deep learning, computational pathology has made significant progress in cancer diagnosis and subtyping. Tissue segmentation is a core challenge, essential for prognosis and treatment decisio…
PrognosisSegmentationSemantic SegmentationWeakly supervised Semantic Segmentation+1Weakly Supervised Semantic Segmentation via Progressive Confidence Region Expansion
Weakly supervised semantic segmentation (WSSS) has garnered considerable attention due to its effective reduction of annotation costs. Most approaches utilize Class Activation Maps (CAM) to produce pseudo-labels, the…
Inductive BiasSemantic SegmentationWeakly supervised Semantic SegmentationWeakly-Supervised Semantic SegmentationPOT: Prototypical Optimal Transport for Weakly Supervised Semantic Segmentation
Weakly Supervised Semantic Segmentation (WSSS) leverages Class Activation Maps (CAMs) to extract spatial information from image-level labels. However, CAMs primarily highlight the most discriminative foreground regio…
Semantic SegmentationWeakly supervised Semantic SegmentationWeakly-Supervised Semantic SegmentationFFR: Frequency Feature Rectification for Weakly Supervised Semantic Segmentation
Image-level Weakly Supervised Semantic Segmentation (WSSS) has garnered significant attention due to its low annotation costs. Current single-stage state-of-the-art WSSS methods mainly rely on V ision T ransformer …
DecoderSegmentationSemantic SegmentationWeakly supervised Semantic Segmentation+1Multi-Label Prototype Visual Spatial Search for Weakly Supervised Semantic Segmentation
Existing Weakly Supervised Semantic Segmentation (WSSS) relies on the CNN-based Class Activation Map (CAM) and Transformer-based self-attention map to generate class-specific masks for semantic segmentation. However,…
Semantic SegmentationWeakly supervised Semantic SegmentationWeakly-Supervised Semantic SegmentationHisynSeg: Weakly-Supervised Histopathological Image Segmentation via Image-Mixing Synthesis and Consistency Regularization
Tissue semantic segmentation is one of the key tasks in computational pathology. To avoid the expensive and laborious acquisition of pixel-level annotations, a wide range of studies attempt to adopt the class activation …
Image SegmentationSegmentationSemantic SegmentationWeakly-supervised Learning+2Image Augmentation Agent for Weakly Supervised Semantic Segmentation
Weakly-supervised semantic segmentation (WSSS) has achieved remarkable progress using only image-level labels. However, most existing WSSS methods focus on designing new network structures and loss functions to generate …
Image AugmentationSemantic SegmentationWeakly supervised Semantic SegmentationWeakly-Supervised Semantic SegmentationToward Modality Gap: Vision Prototype Learning for Weakly-supervised Semantic Segmentation with CLIP
The application of Contrastive Language-Image Pre-training (CLIP) in Weakly Supervised Semantic Segmentation (WSSS) research powerful cross-modal semantic understanding capabilities. Existing methods attempt to optimize …
Semantic SegmentationWeakly supervised Semantic SegmentationWeakly-Supervised Semantic SegmentationPrompt Categories Cluster for Weakly Supervised Semantic Segmentation
Weakly Supervised Semantic Segmentation (WSSS), which leverages image-level labels, has garnered significant attention due to its cost-effectiveness. The previous methods mainly strengthen the inter-class differences to …
Semantic SegmentationWeakly supervised Semantic SegmentationWeakly-Supervised Semantic SegmentationMoRe: Class Patch Attention Needs Regularization for Weakly Supervised Semantic Segmentation
Weakly Supervised Semantic Segmentation (WSSS) with image-level labels typically uses Class Activation Maps (CAM) to achieve dense predictions. Recently, Vision Transformer (ViT) has provided an alternative to generate l…
Semantic SegmentationWeakly supervised Semantic SegmentationWeakly-Supervised Semantic SegmentationRevisiting the Integration of Convolution and Attention for Vision Backbone
Convolutions (Convs) and multi-head self-attentions (MHSAs) are typically considered alternatives to each other for building vision backbones. Although some works try to integrate both, they apply the two operators simul…
Semantic SegmentationWeakly supervised Semantic SegmentationWeakly-Supervised Semantic SegmentationA Multimodal Approach Combining Structural and Cross-domain Textual Guidance for Weakly Supervised OCT Segmentation
Accurate segmentation of Optical Coherence Tomography (OCT) images is crucial for diagnosing and monitoring retinal diseases. However, the labor-intensive nature of pixel-level annotation limits the scalability of superv…
DescriptiveDiagnosticSegmentationSemantic Segmentation+2Enhancing Weakly Supervised Semantic Segmentation for Fibrosis via Controllable Image Generation
Fibrotic Lung Disease (FLD) is a severe condition marked by lung stiffening and scarring, leading to respiratory decline. High-resolution computed tomography (HRCT) is critical for diagnosing and monitoring FLD; however,…
Image GenerationSemantic SegmentationWeakly supervised Semantic SegmentationWeakly-Supervised Semantic Segmentation