Papers Pseudo Label
“Pseudo Label” 태그가 달린 논문 956편 · 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+2FedGSCA: Medical Federated Learning with Global Sample Selector and Client Adaptive Adjuster under Label Noise
Federated Learning (FL) emerged as a solution for collaborative medical image classification while preserving data privacy. However, label noise, which arises from inter-institutional data variability, can cause training…
Federated Learningimage-classificationImage ClassificationMedical Image Classification+1Leveraging 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 SegmentationQUEST: Quality-aware Semi-supervised Table Extraction for Business Documents
Automating table extraction (TE) from business documents is critical for industrial workflows but remains challenging due to sparse annotations and error-prone multi-stage pipelines. While semi-supervised learning (SSL) …
Pseudo LabelTable ExtractionFlick: Few Labels Text Classification using K-Aware Intermediate Learning in Multi-Task Low-Resource Languages
Training deep learning networks with minimal supervision has gained significant research attention due to its potential to reduce reliance on extensive labelled data. While self-training methods have proven effective in …
Domain AdaptationPseudo Labeltext-classificationText ClassificationRobust Unsupervised Adaptation of a Speech Recogniser Using Entropy Minimisation and Speaker Codes
Speech recognisers usually perform optimally only in a specific environment and need to be adapted to work well in another. For adaptation to a new speaker, there is often too little data for fine-tuning to be robust, an…
Pseudo LabelSRPL-SFDA: SAM-Guided Reliable Pseudo-Labels for Source-Free Domain Adaptation in Medical Image Segmentation
Domain Adaptation (DA) is crucial for robust deployment of medical image segmentation models when applied to new clinical centers with significant domain shifts. Source-Free Domain Adaptation (SFDA) is appealing as it ca…
Domain AdaptationImage SegmentationMedical Image SegmentationPseudo Label+2Segment Concealed Objects with Incomplete Supervision
Incompletely-Supervised Concealed Object Segmentation (ISCOS) involves segmenting objects that seamlessly blend into their surrounding environments, utilizing incompletely annotated data, such as weak and semi-annotation…
Pseudo LabelSegmentationSemantic SegmentationMultiMatch: Multihead Consistency Regularization Matching for Semi-Supervised Text Classification
We introduce MultiMatch, a novel semi-supervised learning (SSL) algorithm combining the paradigms of co-training and consistency regularization with pseudo-labeling. At its core, MultiMatch features a three-fold pseudo-l…
Pseudo LabelSemi-Supervised Text Classificationtext-classificationText ClassificationFrame-Level Real-Time Assessment of Stroke Rehabilitation Exercises from Video-Level Labeled Data: Task-Specific vs. Foundation Models
The growing demands of stroke rehabilitation have increased the need for solutions to support autonomous exercising. Virtual coaches can provide real-time exercise feedback from video data, helping patients improve motor…
Pseudo LabelL3A: Label-Augmented Analytic Adaptation for Multi-Label Class Incremental Learning
Class-incremental learning (CIL) enables models to learn new classes continually without forgetting previously acquired knowledge. Multi-label CIL (MLCIL) extends CIL to a real-world scenario where each sample may belong…
class-incremental learningClass Incremental LearningExemplar-FreeIncremental Learning+2Shuffle PatchMix Augmentation with Confidence-Margin Weighted Pseudo-Labels for Enhanced Source-Free Domain Adaptation
This work investigates Source-Free Domain Adaptation (SFDA), where a model adapts to a target domain without access to source data. A new augmentation technique, Shuffle PatchMix (SPM), and a novel reweighting strategy a…
Data AugmentationDomain AdaptationPseudo LabelSource-Free Domain AdaptationD2AF: A Dual-Driven Annotation and Filtering Framework for Visual Grounding
Visual Grounding is a task that aims to localize a target region in an image based on a free-form natural language description. With the rise of Transformer architectures, there is an increasing need for larger datasets …
DiversityPseudo LabelVisual GroundingCollaborative Learning for Unsupervised Multimodal Remote Sensing Image Registration: Integrating Self-Supervision and MIM-Guided Diffusion-Based Image Translation
The substantial modality-induced variations in radiometric, texture, and structural characteristics pose significant challenges for the accurate registration of multimodal images. While supervised deep learning methods h…
Image RegistrationPseudo LabelCAST: Contrastive Adaptation and Distillation for Semi-Supervised Instance Segmentation
Instance segmentation demands costly per-pixel annotations and large models. We introduce CAST, a semi-supervised knowledge distillation (SSKD) framework that compresses pretrained vision foundation models (VFM) into com…
Domain AdaptationInstance SegmentationKnowledge DistillationPseudo Label+2RefAV: Towards Planning-Centric Scenario Mining
Autonomous Vehicles (AVs) collect and pseudo-label terabytes of multi-modal data localized to HD maps during normal fleet testing. However, identifying interesting and safety-critical scenarios from uncurated driving log…
Autonomous VehiclesMotion PlanningNatural Language QueriesPseudo LabelSemi-Supervised Conformal Prediction With Unlabeled Nonconformity Score
Conformal prediction (CP) is a powerful framework for uncertainty quantification, providing prediction sets with coverage guarantees when calibrated on sufficient labeled data. However, in real-world applications where l…
Conformal PredictionPredictionPseudo LabelUncertainty QuantificationCertainty and Uncertainty Guided Active Domain Adaptation
Active Domain Adaptation (ADA) adapts models to target domains by selectively labeling a few target samples. Existing ADA methods prioritize uncertain samples but overlook confident ones, which often match ground-truth. …
Domain AdaptationPseudo LabelA Square Peg in a Square Hole: Meta-Expert for Long-Tailed Semi-Supervised Learning
This paper studies the long-tailed semi-supervised learning (LTSSL) with distribution mismatch, where the class distribution of the labeled training data follows a long-tailed distribution and mismatches with that of the…
Pseudo LabelViaRL: Adaptive Temporal Grounding via Visual Iterated Amplification Reinforcement Learning
Video understanding is inherently intention-driven-humans naturally focus on relevant frames based on their goals. Recent advancements in multimodal large language models (MLLMs) have enabled flexible query-driven reason…
Pseudo LabelReinforcement Learning (RL)Video Understanding