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An efficient weakly semi-supervised method for object automated annotation
Object annotation is essential for computer vision tasks, and more high-quality annotated data can effectively improve the performance of vision...
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A survey of class-imbalanced semi-supervised learning
Semi-supervised learning(SSL) can substantially improve the performance of deep neural networks by utilizing unlabeled data when labeled data is...
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Learning sample-aware threshold for semi-supervised learning
Pseudo-labeling methods are popular in semi-supervised learning (SSL). Their performance heavily relies on a proper threshold to generate hard labels...
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CISO: Co-iteration semi-supervised learning for visual object detection
Semi-supervised learning offers a solution to the high cost and limited availability of manually labeled samples in supervised learning. In...
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Weakly/Semi-supervised Left Ventricle Segmentation in 2D Echocardiography with Uncertain Region-Aware Contrastive Learning
Segmentation of the left ventricle in 2D echocardiography is essential for cardiac function measures, such as ejection fraction. Fully-supervised...
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Learning Self-supervised Low-Rank Network for Single-Stage Weakly and Semi-supervised Semantic Segmentation
Semantic segmentation with limited annotations, such as weakly supervised semantic segmentation (WSSS) and semi-supervised semantic segmentation...
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Weakly Labeled Semi-Supervised Sound Event Detection Based on Convolutional Independent Recurrent Neural Networks
AbstractSound Event Detection (SED) needs to identify the sound events in a recording and detect the onset and offset times of them. Deep...
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A systematic review for class-imbalance in semi-supervised learning
This review aims to examine the state of the art of semi-supervised learning (SSL) techniques for addressing class imbalanced data. Class imbalance...
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SemiDocSeg: harnessing semi-supervised learning for document layout analysis
Document Layout Analysis (DLA) is the process of automatically identifying and categorizing the structural components (e.g. Text, Figure, Table,...
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Semi-supervised partial label learning algorithm via reliable label propagation
Partial label learning (PLL) is a weakly supervised learning method that is able to predict one label as the correct answer from a given candidate...
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Revisiting Consistency Regularization for Semi-Supervised Learning
Consistency regularization is one of the most widely-used techniques for semi-supervised learning (SSL). Generally, the aim is to train a model that...
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SoftCTC—semi-supervised learning for text recognition using soft pseudo-labels
This paper explores semi-supervised training for sequence tasks, such as optical character recognition or automatic speech recognition. We propose a...
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Exploration and Exploitation of Unlabeled Data for Open-Set Semi-supervised Learning
In this paper, we address a complex but practical scenario in semi-supervised learning (SSL) named open-set SSL, where unlabeled data contain both...
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Adversarial representation teaching with perturbation-agnostic student-teacher structure for semi-supervised learning
Consistency regularization (CR) is representative semi-supervised learning (SSL) technique that maintains the consistency of predictions from...
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Weakly Semi-supervised Detection in Lung Ultrasound Videos
Frame-by-frame annotation of bounding boxes by clinical experts is often required to train fully supervised object detection models on medical video...
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A review of semi-supervised learning for text classification
A huge amount of data is generated daily leading to big data challenges. One of them is related to text mining, especially text classification. To...
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Semi-supervised Learning with Nearest-Neighbor Label and Consistency Regularization
Semi-supervised learning, a system dedicated to making networks less dependent on labeled data, has become a popular paradigm due to its strong...
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A critical study on the recent deep learning based semi-supervised video anomaly detection methods
Video anomaly detection (VAD) is currently a trending research area within computer vision, given that anomalies form a key detection objective in...
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NCMatch: Semi-supervised Learning with Noisy Labels via Noisy Sample Filter and Contrastive Learning
Semi-supervised learning (SSL) has been widely studied in recent years, which aims to improve the performance of supervised learning by utilizing...
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SPL-LDP: a label distribution propagation method for semi-supervised partial label learning
Partial label learning learns from examples represented by a single instance while associated with multiple candidate labels, among which only one...
