Papers Semi-Supervised Semantic Segmentation
“Semi-Supervised Semantic Segmentation” 태그가 달린 논문 205편 · 필터 해제
Bridging the Gap between Labeled and Unlabeled Data via Unified Flow with Feature Memory Bank
Although semi-supervised semantic segmentation ($\text{S}^4$) utilizes abundant unlabeled data to reduce manual labeling burdens, independent training of labeled and unlabeled data causes the former to dominate, which se…
Semi-Supervised Semantic SegmentationCW-BASS v2: Saturation-Aware Pseudo-Label Selection for Semi-Supervised Segmentation under Foundation-Model Teachers
Semi-supervised semantic segmentation has long turned on one question, which pseudo-labels to trust, and a generation of selection rules, dynamic thresholds, per-class curricula, soft confidence weights, answered it for …
Semi-Supervised Semantic SegmentationPixCon: Clean-Positive Contrastive Learning for Foundation-Model Semi-Supervised Segmentation
Semi-supervised semantic segmentation (SSSS) has long turned on one question, which pseudo-labels to trust, and answered it with ever more careful confidence filtering. Foundation backbones change the regime: with a DINO…
Semi-Supervised Semantic SegmentationContrastive LearningBidirectional Fusion Guided by Cardiac Patterns for Semi-Supervised ECG Segmentation
Accurate delineation of electrocardiogram (ECG), the segmentation of meaningful waveform features, is crucial for cardiovascular diagnostics. However, the scarcity of annotated data poses a significant challenge for trai…
Semi-Supervised Semantic SegmentationUniSemAlign: Text-Prototype Alignment with a Foundation Encoder for Semi-Supervised Histopathology Segmentation
Semi-supervised semantic segmentation in computational pathology remains challenging due to scarce pixel-level annotations and unreliable pseudo-label supervision. We propose UniSemAlign, a dual-modal semantic alignment …
Semi-Supervised Semantic SegmentationVision-Language Model Purified Semi-Supervised Semantic Segmentation for Remote Sensing Images
The semi-supervised semantic segmentation (S4) can learn rich visual knowledge from low-cost unlabeled images. However, traditional S4 architectures all face the challenge of low-quality pseudo-labels, especially for the…
Semi-Supervised Semantic SegmentationBoosting Overlapping Organoid Instance Segmentation Using Pseudo-Label Unmixing and Synthesis-Assisted Learning
Organoids, sophisticated in vitro models of human tissues, are crucial for medical research due to their ability to simulate organ functions and assess drug responses accurately. Accurate organoid instance segmentation i…
Semi-Supervised Semantic SegmentationInstance SegmentationWhen Confidence Fails: Revisiting Pseudo-Label Selection in Semi-supervised Semantic Segmentation
While significant advances exist in pseudo-label generation for semi-supervised semantic segmentation, pseudo-label selection remains understudied. Existing methods typically use fixed confidence thresholds to retain hig…
Semi-Supervised Semantic SegmentationGraph-Theoretic Consistency for Robust and Topology-Aware Semi-Supervised Histopathology Segmentation
Semi-supervised semantic segmentation (SSSS) is vital in computational pathology, where dense annotations are costly and limited. Existing methods often rely on pixel-level consistency, which propagates noisy pseudo-labe…
Semi-Supervised Semantic SegmentationHarnessing Group-Oriented Consistency Constraints for Semi-Supervised Semantic Segmentation in CdZnTe Semiconductors
Labeling Cadmium Zinc Telluride (CdZnTe) semiconductor images is challenging due to the low-contrast defect boundaries, necessitating annotators to cross-reference multiple views. These views share a single ground truth …
Semi-Supervised Semantic SegmentationS5: Scalable Semi-Supervised Semantic Segmentation in Remote Sensing
Semi-supervised semantic segmentation (S4) has advanced remote sensing (RS) analysis by leveraging unlabeled data through pseudo-labeling and consistency learning. However, existing S4 studies often rely on small-scale d…
Semi-Supervised Semantic SegmentationObject DetectionSemi-Supervised Semantic Segmentation via Derivative Label Propagation
Semi-supervised semantic segmentation, which leverages a limited set of labeled images, helps to relieve the heavy annotation burden. While pseudo-labeling strategies yield promising results, there is still room for enha…
Semi-Supervised Semantic SegmentationSemiSegECG: A Multi-Dataset Benchmark for Semi-Supervised Semantic Segmentation in ECG Delineation
Electrocardiogram (ECG) delineation, the segmentation of meaningful waveform features, is critical for clinical diagnosis. Despite recent advances using deep learning, progress has been limited by the scarcity of publicl…
Semi-Supervised Semantic SegmentationConformalSAM: Unlocking the Potential of Foundational Segmentation Models in Semi-Supervised Semantic Segmentation with Conformal Prediction
Pixel-level vision tasks, such as semantic segmentation, require extensive and high-quality annotated data, which is costly to obtain. Semi-supervised semantic segmentation (SSSS) has emerged as a solution to alleviate t…
Semi-Supervised Semantic SegmentationSAMST: A Transformer framework based on SAM pseudo label filtering for remote sensing semi-supervised semantic segmentation
Public remote sensing datasets often face limitations in universality due to resolution variability and inconsistent land cover category definitions. To harness the vast pool of unlabeled remote sensing data, we propose …
Boundary DetectionPseudo LabelPseudo Label FilteringSemantic Segmentation+2DEARLi: Decoupled Enhancement of Recognition and Localization for Semi-supervised Panoptic Segmentation
Pixel-level annotation is expensive and time-consuming. Semi-supervised segmentation methods address this challenge by learning models on few labeled images alongside a large corpus of unlabeled images. Although foundati…
DecoderGPUPanoptic SegmentationSemantic Segmentation+3Leveraging Out-of-Distribution Unlabeled Images: Semi-Supervised Semantic Segmentation with an Open-Vocabulary Model
In semi-supervised semantic segmentation, existing studies have shown promising results in academic settings with controlled splits of benchmark datasets. However, the potential benefits of leveraging significantly large…
Pseudo LabelSegmentationSemantic SegmentationSemi-Supervised Semantic SegmentationLeveraging Out-of-Distribution Unlabeled Images: Semi-Supervised Semantic Segmentation with an Open-Vocabulary Model
In semi-supervised semantic segmentation, existing studies have shown promising results in academic settings with controlled splits of benchmark datasets. However, the potential benefits of leveraging significantly large…
Semi-Supervised Semantic SegmentationHierVL: Semi-Supervised Segmentation leveraging Hierarchical Vision-Language Synergy with Dynamic Text-Spatial Query Alignment
Semi-supervised semantic segmentation remains challenging under severe label scarcity and domain variability. Vision-only methods often struggle to generalize, resulting in pixel misclassification between similar classes…
Semantic SegmentationSemi-Supervised Semantic SegmentationFARCLUSS: Fuzzy Adaptive Rebalancing and Contrastive Uncertainty Learning for Semi-Supervised Semantic Segmentation
Semi-supervised semantic segmentation (SSSS) faces persistent challenges in effectively leveraging unlabeled data, such as ineffective utilization of pseudo-labels, exacerbation of class imbalance biases, and neglect of …
Semantic SegmentationSemi-Supervised Semantic Segmentation