Papers Pseudo Label Filtering
“Pseudo Label Filtering” 태그가 달린 논문 26편 · 필터 해제
SAMST: 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+2Feedback-Driven Pseudo-Label Reliability Assessment: Redefining Thresholding for Semi-Supervised Semantic Segmentation
Semi-supervised learning leverages unlabeled data to enhance model performance, addressing the limitations of fully supervised approaches. Among its strategies, pseudo-supervision has proven highly effective, typically r…
Pseudo LabelPseudo Label FilteringSemantic SegmentationSemi-Supervised Semantic SegmentationConfidence-Weighted Boundary-Aware Learning for Semi-Supervised Semantic Segmentation
Semi-supervised semantic segmentation (SSSS) aims to improve segmentation performance by utilising unlabeled data alongside limited labeled samples. Existing SSSS methods often face challenges such as coupling, where ove…
Pseudo Label FilteringSegmentationSemantic SegmentationSemi-Supervised Semantic SegmentationSnowMaster: Comprehensive Real-world Image Desnowing via MLLM with Multi-Model Feedback Optimization
Snowfall presents significant challenges for visual data processing, necessitating specialized desnowing algorithms. However, existing models often fail to generalize effectively due to their heavy reliance on synthe…
Pseudo LabelPseudo Label FilteringSeek Common Ground While Reserving Differences: Semi-Supervised Image-Text Sentiment Recognition
Multimodal sentiment analysis has attracted extensive research attention as increasing users share images and texts to express their emotions and opinions on social media. Collecting large amounts of labeled sentimen…
Multimodal Sentiment AnalysisPseudo LabelPseudo Label FilteringSentiment AnalysisEnergy Score-based Pseudo-Label Filtering and Adaptive Loss for Imbalanced Semi-supervised SAR target recognition
Automatic target recognition (ATR) is an important use case for synthetic aperture radar (SAR) image interpretation. Recent years have seen significant advancements in SAR ATR technology based on semi-supervised learning…
Pseudo LabelPseudo Label FilteringTripletPMT: Progressive Mean Teacher via Exploring Temporal Consistency for Semi-Supervised Medical Image Segmentation
Semi-supervised learning has emerged as a widely adopted technique in the field of medical image segmentation. The existing works either focuses on the construction of consistency constraints or the generation of pseudo …
Image SegmentationMedical Image SegmentationPseudo LabelPseudo Label Filtering+2Less is More: Pseudo-Label Filtering for Continual Test-Time Adaptation
Continual Test-Time Adaptation (CTTA) aims to adapt a pre-trained model to a sequence of target domains during the test phase without accessing the source data. To adapt to unlabeled data from unknown domains, existing m…
DiversityPseudo LabelPseudo Label FilteringTest-time AdaptationSparse Semi-DETR: Sparse Learnable Queries for Semi-Supervised Object Detection
In this paper, we address the limitations of the DETR-based semi-supervised object detection (SSOD) framework, particularly focusing on the challenges posed by the quality of object queries. In DETR-based SSOD, the one-t…
Objectobject-detectionObject DetectionPseudo Label+2Uncertainty-Aware Pseudo-Label Filtering for Source-Free Unsupervised Domain Adaptation
Source-free unsupervised domain adaptation (SFUDA) aims to enable the utilization of a pre-trained source model in an unlabeled target domain without access to source data. Self-training is a way to solve SFUDA, where co…
Contrastive LearningDomain AdaptationMemorizationPseudo Label+2FishSegSSL: A Semi-Supervised Semantic Segmentation Framework for Fish-Eye Images
The application of large field-of-view (FoV) cameras equipped with fish-eye lenses brings notable advantages to various real-world computer vision applications, including autonomous driving. While deep learning has prove…
Autonomous DrivingImage SegmentationPseudo LabelPseudo Label Filtering+3Data-Free Quantization via Pseudo-label Filtering
Quantization for model compression can efficiently reduce the network complexity and storage requirement but the original training data is necessary to remedy the performance loss caused by quantization. The Data-Fre…
Data Free QuantizationModel CompressionPseudo LabelPseudo Label Filtering+1EC-Depth: Exploring the consistency of self-supervised monocular depth estimation in challenging scenes
Self-supervised monocular depth estimation holds significant importance in the fields of autonomous driving and robotics. However, existing methods are typically trained and tested on standard datasets, overlooking the i…
Autonomous DrivingDepth EstimationDepth PredictionMonocular Depth Estimation+3Towards Self-Adaptive Pseudo-Label Filtering for Semi-Supervised Learning
Recent semi-supervised learning (SSL) methods typically include a filtering strategy to improve the quality of pseudo labels. However, these filtering strategies are usually hand-crafted and do not change as the model is…
Pseudo LabelPseudo Label FilteringAttentive Continuous Generative Self-training for Unsupervised Domain Adaptive Medical Image Translation
Self-training is an important class of unsupervised domain adaptation (UDA) approaches that are used to mitigate the problem of domain shift, when applying knowledge learned from a labeled source domain to unlabeled and …
Domain AdaptationPseudo LabelPseudo Label FilteringTranslation+2COSST: Multi-organ Segmentation with Partially Labeled Datasets Using Comprehensive Supervisions and Self-training
Deep learning models have demonstrated remarkable success in multi-organ segmentation but typically require large-scale datasets with all organs of interest annotated. However, medical image datasets are often low in sam…
Computed Tomography (CT)Medical Image SegmentationOrgan SegmentationPartial Label Learning+3Semi-Supervised Semantic Segmentation With Region Relevance
Semi-supervised semantic segmentation aims to learn from a small amount of labeled data and plenty of unlabeled ones for the segmentation task. The most common approach is to generate pseudo-labels for unlabeled images t…
DiversityPseudo LabelPseudo Label FilteringSegmentation+2Probabilistic Domain Adaptation for Biomedical Image Segmentation
Segmentation is a key analysis tasks in biomedical imaging. Given the many different experimental settings in this field, the lack of generalization limits the use of deep learning in practice. Domain adaptation is a pro…
Domain AdaptationImage SegmentationPseudo LabelPseudo Label Filtering+2Joint Speech Transcription and Translation: Pseudo-Labeling with Out-of-Distribution Data
Self-training has been shown to be helpful in addressing data scarcity for many domains, including vision, speech, and language. Specifically, self-training, or pseudo-labeling, labels unsupervised data and adds that to …
Data AugmentationPseudo LabelPseudo Label FilteringTranslationSemi-Supervised Object Detection with Object-wise Contrastive Learning and Regression Uncertainty
Semi-supervised object detection (SSOD) aims to boost detection performance by leveraging extra unlabeled data. The teacher-student framework has been shown to be promising for SSOD, in which a teacher network generates …
ClassificationContrastive LearningObjectobject-detection+7