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Semi-Supervised Semantic Segmentation
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Models that are trained with a small number of labeled examples and a large number of unlabeled examples and whose aim is to learn to segment an image (i.e. assign a class to every pixel).
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
DEARLi: Decoupled Enhancement of Recognition and Localization for Semi-supervised Panoptic Segmentation
Leveraging Out-of-Distribution Unlabeled Images: Semi-Supervised Semantic Segmentation with an Open-Vocabulary Model
HierVL: Semi-Supervised Segmentation leveraging Hierarchical Vision-Language Synergy with Dynamic Text-Spatial Query Alignment
FARCLUSS: Fuzzy Adaptive Rebalancing and Contrastive Uncertainty Learning for Semi-Supervised Semantic Segmentation
RS-MTDF: Multi-Teacher Distillation and Fusion for Remote Sensing Semi-Supervised Semantic Segmentation
Adaptive Spatial Augmentation for Semi-supervised Semantic Segmentation
Zero-Shot Pseudo Labels Generation Using SAM and CLIP for Semi-Supervised Semantic Segmentation
Feedback-Driven Pseudo-Label Reliability Assessment: Redefining Thresholding for Semi-Supervised Semantic Segmentation
IGL-DT: Iterative Global-Local Feature Learning with Dual-Teacher Semantic Segmentation Framework under Limited Annotation Scheme
BoundMatch: Boundary detection applied to semi-supervised segmentation for urban-driving scenes
Semi-supervised Semantic Segmentation with Multi-Constraint Consistency Learning
Knowledge Consultation for Semi-Supervised Semantic Segmentation
Exploring Token-Level Augmentation in Vision Transformer for Semi-Supervised Semantic Segmentation
Confidence-Weighted Boundary-Aware Learning for Semi-Supervised Semantic Segmentation
Semi-supervised Semantic Segmentation for Remote Sensing Images via Multi-scale Uncertainty Consistency and Cross-Teacher-Student Attention
Uncertainty and Energy based Loss Guided Semi-Supervised Semantic Segmentation
Uncertainty-Participation Context Consistency Learning for Semi-supervised Semantic Segmentation
Biologically-inspired Semi-supervised Semantic Segmentation for Biomedical Imaging
Masked Image Modeling Boosting Semi-Supervised Semantic Segmentation
Revisiting Network Perturbation for Semi-Supervised Semantic Segmentation
UniMatch V2: Pushing the Limit of Semi-Supervised Semantic Segmentation
Learning from Spatio-temporal Correlation for Semi-Supervised LiDAR Semantic Segmentation
Exploiting Minority Pseudo-Labels for Semi-Supervised Semantic Segmentation in Autonomous Driving
Exploring Scene Affinity for Semi-Supervised LiDAR Semantic Segmentation
Weighting Pseudo-Labels via High-Activation Feature Index Similarity and Object Detection for Semi-Supervised Segmentation
Beyond Pixels: Semi-Supervised Semantic Segmentation with a Multi-scale Patch-based Multi-Label Classifier
SS-ADA: A Semi-Supervised Active Domain Adaptation Framework for Semantic Segmentation
Revisiting and Maximizing Temporal Knowledge in Semi-supervised Semantic Segmentation
IPixMatch: Boost Semi-supervised Semantic Segmentation with Inter-Pixel Relation
PV-S3: Advancing Automatic Photovoltaic Defect Detection using Semi-Supervised Semantic Segmentation of Electroluminescence Images
Multi-Level Label Correction by Distilling Proximate Patterns for Semi-supervised Semantic Segmentation
Towards the Uncharted: Density-Descending Feature Perturbation for Semi-supervised Semantic Segmentation
AllSpark: Reborn Labeled Features from Unlabeled in Transformer for Semi-Supervised Semantic Segmentation
Semi-Supervised Semantic Segmentation Based on Pseudo-Labels: A Survey
PRCL: Probabilistic Representation Contrastive Learning for Semi-Supervised Semantic Segmentation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.