An incremental learning framework combining sample confidence and discrimination with an application to automatic image annotation.

By naturally combining two recently rising techniques: active learning (AL) and self-paced learning (SPL), our framework is capable of automatically annotating new instances and incorporating them into ... This paper aims to develop a novel cost-effective framework for face identification, which progressively maintains a batch of classifiers with the increasing face images of different individuals. ... As for the commercial product, the website "Face.com" once provided an API (application interface) to automatically detect and recognize faces in photos. ...

doi:10.1109/tpami.2017.2652459 pmid:28092522 fatcat:wvcef7tsx5djfmxq6t25ms4rf4

Multiple Versions

In this paper, we propose a deep framework to incrementally and actively profile in-the-wild complex emotions, from sparse data. ... Therefore, it is important to learn the profile of the in-the-wild complex emotions accurately using limited annotated samples. ... The intuition behind this algorithm is to select the samples with high confidence values to automatically annotate and add them into the training set. ...

doi:10.1109/access.2020.3015917 fatcat:xbtqi423nfaolkk3vturgpaqpe

DOAJ

In this paper, we introduce an active annotation and learning framework for the face recognition task. ... Starting with an initial label deficient face image training set, we iteratively train a deep neural network and use this model to choose the examples for further manual annotation. ... Acknowledgment This work was supported in part by grants from Natural Science Foundation of China (No. 61572138) and Science and Technology Commission of Shanghai Municipality (No. 16511104802). ...

doi:10.1145/2964284.2984059 dblp:conf/mm/YeSWMWZX16 fatcat:jmjzpsnxnbcd3lcyilayfvrnxe

With ever increasing amounts of annotated medical datasets, it is infeasible to train a learning method always with all data from scratch. ... In this paper, we introduce a framework for applying incremental learning for segmentation and propose novel methods for selecting representative data therein. ... sample is represented with a class-discriminating vector. ...

doi:10.1007/978-3-030-00937-3_42 fatcat:3wyfgbqf7fctzko5tqztsvgf7e

Multiple Versions

in an incremental learning manner. ... Specifically, these high confidence samples are automatically selected and iteratively assigned pseudo-labels. ... Thus it has great practical significance to develop a framework by combining CNNs and active learning, which can jointly learn features and classifiers/models from unlabeled training data with minimal ...

doi:10.1109/tcsvt.2016.2589879 fatcat:6vc55x3cjbhyxb4jcntirob57y

Multiple Versions

Compared with conventional latent models, the proposed PLM incorporates a spatial regularization term to reduce ambiguities in object proposals and to enforce object localization, and also a graph-based ... Extensive experiments demonstrate that even without annotation the proposed self-learning approach outperforms weakly supervised learning approaches, while achieving comparable performance with transfer ... Tekes and Infotech Oulu are gratefully acknowledged. ...

doi:10.1109/cvpr.2017.222 dblp:conf/cvpr/YeZKQCSZ17 fatcat:4b6uc7qvfvhkhj5gljgim5a7ai

In this paper we present an incremental framework aimed at extracting field information from administrative document images in the context of a Digital Mail-room scenario. ... This model is incrementally refined as the system processes more and more documents from the same class. ... Finally, if the confidence of the extracted field is high enough, an incremental step is applied in order to adjust the learned structural model. ...

doi:10.1109/icdar.2013.223 dblp:conf/icdar/RusinolBD13 fatcat:dxa5wu337rd4nfoi7emy4pmcfa

Automatically detecting mud in bauxite ores is important and valuable, with which we can improve productivity and reduce pollution. ... The method uses the YOLO-v3 model as the basic detector, which is initialized with the pretrained weights on the MS COCO dataset. Then, an AL framework-embedded YOLO-v3 model is constructed. ... Acknowledgments is study was supported by the Scientific Research Fund of Hunan Provincial Education Department (nos. 18A376 and XJK17BXX010) and National Natural Science Foundation of China (no. 11701172 ...

doi:10.1155/2020/3510313 fatcat:6klk3wmobbfr3ni3l4e6gtxuvm

DOAJ Szczepanski

This problem has been recently explored and has rapidly grown with a large number of ad-hoc approaches. ... analysis of the classifier discrepancies, self-teaching, entropy minimization, curriculum learning and multi-task learning. ... Target segmentation maps are then directly filtered according to some confidence-based thresholding policy and used in combination with original source annotated data for the supervised learning of the ...

arXiv:2005.10876v1 fatcat:7t5v6qibxnfcxhwtohqqunhd2u

To satisfactorily personalize the requirement, we propose an interactive image classification system with an offline representation learning stage and an online classification stage. ... To provide a high interactive rate, a unified active learning algorithm is used to search the optimal annotation and verification set by minimizing the expected time cost. ... [19] propose a cost-effective sample selection method to choose many high-confidence samples for deep learning. ...

doi:10.3390/e22111314 pmid:33287081 fatcat:ng2ablbtarfk7kyc27eclzrqri

DOAJ

Compared with conventional latent models, the proposed PLM incorporates a spatial regularization term to reduce ambiguities in object proposals and to enforce object localization, and also a graph-based ... Extensive experiments demonstrate that even without annotation the proposed self-learning approach outperforms weakly supervised learning approaches, while achieving comparable performance with transfer ... Acknowledgement The partial support of this work by ONR, NGA, ARO, NSF, NSFC under Grant 61271433 and 61671427, and Beijing Municipal Science & Technology Commission under Grant Z161100001616005 is gratefully ...

arXiv:1611.07544v1 fatcat:d6fvdn2hjzgurmd2nquvcli7mu

Our approach uses a criterion to select the most relevant samples for annotation by the expert and propagate their label to the most confident samples. ... We introduce a Semi-Supervised Active Learning (SSAL) based on Self-Training (ST) approach for Human Activity Recognition to partially automate the annotation process, reducing the annotation effort and ... ST-SSAL displays during the initial iterations an automatic annotation percentage similar to AL and PL, with only the expert annotator labelling new samples and no automatic annotation, since the 0.98 ...

doi:10.3390/s19030501 fatcat:svws6m4uovfvvkw23asebqxumy

DOAJ

This field has been recently explored and has rapidly grown with a large number of ad-hoc approaches. ... analysis of the classifier discrepancies, self-teaching, entropy minimization, curriculum learning and multi-task learning. ... Target segmentation maps are then directly filtered according to some confidence-based thresholding policy and used in combination with original source annotated data for the supervised learning of the ...

doi:10.3390/technologies8020035 fatcat:qzgjjiw5p5bldk76mh3s3pwlfq

DOAJ

Our framework works incrementally, by learning from easy examples first, and then gradually adapting to harder ones. ... We learn these rich models by collecting training instances for both parts and objects, and automatically connecting the two levels. ... Our framework instead does not require annotations of part positions nor extent, and automatically learns from Google Images instead. Learning from image search engines. ...

doi:10.1109/tpami.2017.2724029 pmid:28692966 fatcat:nibrsnahsfgxffyqj2ozrpgn5i

Multiple Versions

In this paper, we report the development of a methodology and a framework to support labeling, with an application case as background. ... The methodology performs visual active learning and label propagation with 2D embeddings as layouts to achieve faster and interactive labeling of samples. ... Thus, instances with low confidence scores are delivered to expert users to be labeled and instances with high confidence scores are used in the prediction automatically. ...

doi:10.3390/info12070265 fatcat:yvs2a5rdzfdifi3c5gsuozpl3q

DOAJ Szczepanski

Active Self-Paced Learning for Cost-Effective and Progressive Face Identification

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Complex Emotion Profiling: An Incremental Active Learning based Approach with Sparse Annotations

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Face Recognition via Active Annotation and Learning

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Learn the New, Keep the Old: Extending Pretrained Models with New Anatomy and Images [chapter]

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Cost-Effective Active Learning for Deep Image Classification

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Self-Learning Scene-Specific Pedestrian Detectors Using a Progressive Latent Model

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Field Extraction from Administrative Documents by Incremental Structural Templates

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A Weakly Supervised Method for Mud Detection in Ores Based on Deep Active Learning

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Unsupervised Domain Adaptation in Semantic Segmentation: a Review [article]

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Personalized Image Classification by Semantic Embedding and Active Learning

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Self-learning Scene-specific Pedestrian Detectors using a Progressive Latent Model [article]

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A Semi-Automatic Annotation Approach for Human Activity Recognition

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Unsupervised Domain Adaptation in Semantic Segmentation: A Review

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Learning Semantic Part-Based Models from Google Images

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Visual Active Learning for Labeling: A Case for Soundscape Ecology Data

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