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Semi-Supervised Semantic Matching [article]

Zakaria Laskar, Juho Kannala
2019 arXiv   pre-print
Together with the supervised loss the proposed model achieves state-of-the-art on a benchmark semantic matching dataset.  ...  In this paper we propose a semi-supervised learning framework that imposes cyclic consistency constraint on unlabeled image pairs.  ...  Conclusion We presented a semi-supervised learning paradigm to address the problem of semantic matching.  ... 
arXiv:1901.08339v1 fatcat:rckxukrhdjg4fhr2nkrwk2f6sm

Semi-supervised Semantic Matching [chapter]

Zakaria Laskar, Juho Kannala
2019 Lecture Notes in Computer Science  
Together with the supervised loss the proposed model achieves state-ofthe-art on a benchmark semantic matching dataset.  ...  In this paper we propose a semi-supervised learning framework that imposes cyclic consistency constraint on unlabeled image pairs.  ...  Conclusion We presented a semi-supervised learning paradigm to address the problem of semantic matching.  ... 
doi:10.1007/978-3-030-11015-4_32 fatcat:ulvmyxnslbehlinmehjdk7xqci

Progressive Class Semantic Matching for Semi-supervised Text Classification [article]

Hai-Ming Xu and Lingqiao Liu and Ehsan Abbasnejad
2022 arXiv   pre-print
Specifically, we propose a joint semi-supervised learning process that can progressively build a standard K-way classifier and a matching network for the input text and the Class Semantic Representation  ...  Unlike existing approaches that utilize PLMs only for model parameter initialization, we explore the inherent topic matching capability inside PLMs for building a more powerful semi-supervised learning  ...  General Semi-Supervised Learning Semi-supervised learning is a longstanding research topic in machine learning.  ... 
arXiv:2205.10189v1 fatcat:22445mjefng7jmnu5wkzdusuc4

Unsupervised Semantic Aggregation and Deformable Template Matching for Semi-Supervised Learning [article]

Tao Han, Junyu Gao, Yuan Yuan, Qi Wang
2020 arXiv   pre-print
In the meantime, semi-supervised learning (SSL) demonstrates a promising future in leveraging few samples.  ...  Then the semantic labels are aligned to the actual class by the supervision of labeled data.  ...  Semi-supervised Learning. Semi-supervised learning is a mixture of unsupervised and supervised learning [39] .  ... 
arXiv:2010.05517v1 fatcat:aurk4ajdeng5dfjxhdyegq3aam

Content-Based Geospatial Schema Matching Using Semi-supervised Geosemantic Clustering and Hierarchy

Jeffrey Partyka, Latifur Khan
2011 2011 IEEE Fifth International Conference on Semantic Computing  
Semantic similarity across data sources typically involves 1:1 matching of attributes and their instances between tables. Using clustering methods, three distinct challenges remain unaddressed.  ...  Second, a consistent score for an attribute match is not produced. Finally, hierarchical relationships between the data are not considered.  ...  Semi-Supervised Geosemantic Clustering In GeoSim G , semantic similarity is based on semisupervised geosemantic (SSGS) clustering.  ... 
doi:10.1109/icsc.2011.18 dblp:conf/semco/PartykaK11 fatcat:t4uyzhyxfve75dbbzmjzt2wpfy

[Paper] Semantic Concept Detection based on Spatial Pyramid Matching and Semi-supervised Learning

Yoshihiko Kawai, Mahito Fujii
2013 ITE Transactions on Media Technology and Applications  
We also propose a training framework based on semi-supervised learning that uses a small number of labeled data points as a starting point and generates additional labeled training data efficiently, with  ...  Analyzing video for semantic content is very important for finding the desired video among a huge amount of accumulated video data.  ...  We now discuss detection accuracy for the proposed training framework based on semi-supervised learning. We used the average precision for the top 2,000 shots.  ... 
doi:10.3169/mta.1.190 fatcat:lsqfd3zydffpbkvws5ibyeshba

A novel method for measuring semantic similarity for XML schema matching

Buhwan Jeong, Daewon Lee, Hyunbo Cho, Jaewook Lee
2008 Expert systems with applications  
a given few labeled samples in a semi-supervised manner.  ...  To this end, we present a supervised approach to measure semantic similarity between XML schema documents, and, more importantly, address a novel approach to augment reliably labeled training data from  ...  The identification process, so-called semantic matchmaking or schema matching in short, is a reasoning process to produce a set of semantic mappings among input schemas with support of semantic similarity  ... 
doi:10.1016/j.eswa.2007.01.025 fatcat:bjmh3wj75ncpvpeamydd7vl2qy

Semi-Supervised Learning of Semantic Correspondence with Pseudo-Labels [article]

Jiwon Kim, Kwangrok Ryoo, Junyoung Seo, Gyuseong Lee, Daehwan Kim, Hansang Cho, Seungryong Kim
2022 arXiv   pre-print
In this paper, we present a simple, but effective solution for semantic correspondence that learns the networks in a semi-supervised manner by supplementing few ground-truth correspondences via utilization  ...  Traditionally, a supervised learning was used for training the models, which required tremendous manually-labeled data, while some methods suggested a self-supervised or weakly-supervised learning to mitigate  ...  semi-supervised learning.  ... 
arXiv:2203.16038v2 fatcat:wwlf7wqunffirj5exw7zs63m44

Local-driven semi-supervised learning with multi-label

Teng Li, Shuicheng Yan, Tao Mei, In-So Kweon
2009 2009 IEEE International Conference on Multimedia and Expo  
The experiments on multi-label image annotation demonstrate the encouraging results from our proposed framework of semi-supervised learning.  ...  In this paper, we present a local-driven semi-supervised learning framework to propagate the labels of the training data (with multi-label) to the unlabeled data.  ...  By semi-supervised learning the semantic meaning of matching features in the test images is predicted and multi-label for the image is obtained accordingly.  ... 
doi:10.1109/icme.2009.5202790 dblp:conf/icmcs/LiYMK09 fatcat:sdp3q37elra3tjk7dbkzeolguq

Region-level Contrastive and Consistency Learning for Semi-Supervised Semantic Segmentation [article]

Jianrong Zhang, Tianyi Wu, Chuanghao Ding, Hongwei Zhao, Guodong Guo
2022 arXiv   pre-print
Current semi-supervised semantic segmentation methods mainly focus on designing pixel-level consistency and contrastive regularization.  ...  To address the issues, we propose a novel region-level contrastive and consistency learning framework (RC^2L) for semi-supervised semantic segmentation.  ...  ., 2021] are beneficial for semi-supervised semantic segmentation.  ... 
arXiv:2204.13314v1 fatcat:2hpd3rcs7zdezlyhpggwqiddvm

Semi-global Context Network for Semantic Correspondence

Ho-Jun Lee, Hong Tae Choi, Sung Kyu Park, Ho-Hyun Park
2020 IEEE Access  
INDEX TERMS Context fusion, historical averaging, neighborhood consensus network, semantic correspondence, semi-global self-similarity, weakly supervised learning. 2496 This work is licensed under a Creative  ...  We introduce a global context fused feature representation that efficiently employs the global semantic context in estimating semantic correspondence as well as a semi-global self-similarity feature to  ...  We will train the model with other training strategies such as selfsupervised or semi-supervised training methods to train a model with stronger supervision than the weakly supervised method on a small  ... 
doi:10.1109/access.2020.3046845 fatcat:wzikdaus75fxxcfhw7ze6b3nca

Mask-based Data Augmentation for Semi-supervised Semantic Segmentation [article]

Ying Chen, Xu Ouyang, Kaiyue Zhu, Gady Agam
2021 arXiv   pre-print
Semi-supervised learning algorithms address this issue by utilizing unlabeled data and so reduce the amount of labeled data needed for training.  ...  Semantic segmentation using convolutional neural networks (CNN) is a crucial component in image analysis.  ...  PROPOSED SEMI-SUPERVISED LEARNING APPROACH In this section, we present our proposed approach for addressing semi-supervised semantic segmentation.  ... 
arXiv:2101.10156v1 fatcat:nfakhuguk5bhvbfukgtxekvirq

Learning to Learn in a Semi-Supervised Fashion [article]

Yun-Chun Chen, Chao-Te Chou, Yu-Chiang Frank Wang
2020 arXiv   pre-print
To address semi-supervised learning from both labeled and unlabeled data, we present a novel meta-learning scheme.  ...  Thus, our strategy can be viewed as a self-supervised learning scheme, which can be applied to fully supervised learning tasks for improved performance.  ...  In this paper, we propose a novel meta-learning algorithm for image matching in a semi-supervised setting, with applications to image retrieval and person re-ID.  ... 
arXiv:2008.11203v1 fatcat:muvpd6eq6nhbxddwxjpv2ew3u4

Neural Networks for Information Retrieval

Tom Kenter, Alexey Borisov, Christophe Van Gysel, Mostafa Dehghani, Maarten de Rijke, Bhaskar Mitra
2018 Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining - WSDM '18  
Semantic matching II: Semi-and unsupervised semantic matching -60 minutes (MD, CVG). 10 mins Semi-supervised semantic matching -We cover how to model pseudo-labeling using prior knowledge like document  ...  In this session we will focus on semantic matching settings where a supervised signal is available.  ... 
doi:10.1145/3159652.3162009 dblp:conf/wsdm/KenterBGDRM18 fatcat:ybdeuuxcbnh2np34k3y4ve5ovu

Neural Networks for Information Retrieval [article]

Tom Kenter, Alexey Borisov, Christophe Van Gysel, Mostafa Dehghani, Maarten de Rijke, Bhaskar Mitra
2017 arXiv   pre-print
Semantic matching II: Semi-and unsupervised semantic matching -60 minutes (MD, CVG). 10 mins Semi-supervised semantic matching -We cover how to model pseudo-labeling using prior knowledge like document  ...  In this session we will focus on semantic matching settings where a supervised signal is available.  ... 
arXiv:1707.04242v1 fatcat:4idscmq26fa5bjupldwuyghq4m
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