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VIPriors 1: Visual Inductive Priors for Data-Efficient Deep Learning Challenges [article]

Robert-Jan Bruintjes, Attila Lengyel, Marcos Baptista Rios, Osman Semih Kayhan, Jan van Gemert
2021 arXiv   pre-print
We present the first edition of "VIPriors: Visual Inductive Priors for Data-Efficient Deep Learning" challenges.  ...  We offer four data-impaired challenges, where models are trained from scratch, and we reduce the number of training samples to a fraction of the full set.  ...  We focus on data efficiency through visual inductive priors.  ... 
arXiv:2103.03768v1 fatcat:6dyhmwh3unhkrifcrfk5dbphni

DearKD: Data-Efficient Early Knowledge Distillation for Vision Transformers [article]

Xianing Chen, Qiong Cao, Yujie Zhong, Jing Zhang, Shenghua Gao, Dacheng Tao
2022 arXiv   pre-print
However, the excellent performance of transformers heavily depends on enormous training images. Thus, a data-efficient transformer solution is urgently needed.  ...  Our DearKD is a two-stage framework that first distills the inductive biases from the early intermediate layers of a CNN and then gives the transformer full play by training without distillation.  ...  Our main contributions are summarized as follows: • We introduce DearKD, a two-stage learning framework for training vision transformers in a data-efficient manner.  ... 
arXiv:2204.12997v2 fatcat:rralh5mgy5gwrjpp736ocov624

Learning task-agnostic representation via toddler-inspired learning [article]

Kwanyoung Park, Junseok Park, Hyunseok Oh, Byoung-Tak Zhang, Youngki Lee
2021 arXiv   pre-print
Experimental results show that such obtained representation was expandable to various vision tasks such as image classification, object localization, and distance estimation tasks.  ...  To tackle this problem, we derive inspiration from a highly intentional learning system via action: the toddler.  ...  It shows that the toddler-inspired learning framework can efficiently gain a transferrable knowledge of objects with active interaction-based data collection.  ... 
arXiv:2101.11221v1 fatcat:d6fhxvc22fclvcmvkndpyqmgoq

Magnification Prior: A Self-Supervised Method for Learning Representations on Breast Cancer Histopathological Images [article]

Prakash Chandra Chhipa, Richa Upadhyay, Gustav Grund Pihlgren, Rajkumar Saini, Seiichi Uchida, Marcus Liwicki
2022 arXiv   pre-print
factor, inductive transfer, and reducing human prior.  ...  This work presents a novel self-supervised pre-training method to learn efficient representations without labels on histopathology medical images utilizing magnification factors.  ...  Reducing human inducted priors shows efficient selfsupervised representation learning The MPCS-Ordered Pair inducts weaker human prior in pair sampling.  ... 
arXiv:2203.07707v1 fatcat:z7ls32rbonglbiswrjqkawbtym

Adversarial Network Embedding [article]

Quanyu Dai, Qiang Li, Jian Tang, Dan Wang
2017 arXiv   pre-print
Learning low-dimensional representations of networks has proved effective in a variety of tasks such as node classification, link prediction and network visualization.  ...  However, except for objectives to capture network structural properties, most of them suffer from lack of additional constraints for enhancing the robustness of representations.  ...  Liang Zhang of Data Science Lab at JD.com and Prof. Xiaoming Wu of The Hong Kong Polytechnic University for their valuable discussion. Dan Wang's work is supported in part by HK PolyU G-YBAG.  ... 
arXiv:1711.07838v1 fatcat:urnmryjidfgr3di4znm6dh2p2u

Adversarial Network Embedding

Quanyu Dai, Qiang Li, Jian Tang, Dan Wang
2018 PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE  
Learning low-dimensional representations of networks has proved effective in a variety of tasks such as node classification, link prediction and network visualization.  ...  However, except for objectives to capture network structural properties, most of them suffer from lack of additional constraints for enhancing the robustness of representations.  ...  Liang Zhang of Data Science Lab at JD.com and Prof. Xiaoming Wu of The Hong Kong Polytechnic University for their valuable discussion. Dan Wang's work is supported in part by HK PolyU G-YBAG.  ... 
doi:10.1609/aaai.v32i1.11865 fatcat:6mqe7abs4fcrdaje364dwylmsy

Classifier Crafting: Turn Your ConvNet into a Zero-Shot Learner! [article]

Jacopo Cavazza
2021 arXiv   pre-print
The combination of semantic and visual crafting (by simply averaging softmax scores) improves prior state-of-the-art methods in benchmark datasets for standard, inductive ZSL.  ...  visual data are accessible.  ...  raw image data, as opposed to build upon precomputed visual embeddings.  ... 
arXiv:2103.11112v1 fatcat:prram6mddrhypf7dg522crqiwu

MetaFormer: A Unified Meta Framework for Fine-Grained Recognition [article]

Qishuai Diao, Yi Jiang, Bin Wen, Jia Sun, Zehuan Yuan
2022 arXiv   pre-print
To answer this problem, we explore a unified and strong meta-framework(MetaFormer) for fine-grained visual classification.  ...  ., spatio-temporal prior, attribute, and text description) usually appears along with the images.  ...  Conclusion In this work, we propose a unified meta-framework for fine-grained visual classification.  ... 
arXiv:2203.02751v1 fatcat:23kzze24izbjfci4lxfsfrjaua

Special issue on contextual vision computing

Richang Hong, Qi Tian, Nicu Sebe
2014 Machine Vision and Applications  
These papers cover a wide range of subtopics of contextual vision computing, including visual representation, image classification, tag localization, saliency detection, pedestrian detection, and so on  ...  Massive emerging social media data offer new opportunities for resolving the long-standing challenges in computer vision.  ...  Mubarak Shah for providing us the chance to organize this special issue. We thank the reviewers for their great efforts.  ... 
doi:10.1007/s00138-014-0618-1 fatcat:qt7qfz5tk5c3lctgqbnpywr6iy

A bag of words approach for semantic segmentation of monitored scenes

Wassim Bouachir, Atousa Torabi, Guillaume-Alexandre Bilodeau, Pascal Blais
2016 2016 International Symposium on Signal, Image, Video and Communications (ISIVC)  
Then, the second step is based on SIFT keypoints and uses the bag of words representation of the regions for the classification.  ...  This paper proposes a semantic segmentation method for outdoor scenes captured by a surveillance camera.  ...  For region recognition tasks, several induction models use global image features, local visual features, or a combination of both.  ... 
doi:10.1109/isivc.2016.7893967 dblp:conf/isivc/BouachirTBB16 fatcat:bg4f2kg6hfa6renpg7e62oilo4

A Bag of Words Approach for Semantic Segmentation of Monitored Scenes [article]

Wassim Bouachir, Atousa Torabi, Guillaume-Alexandre Bilodeau, Pascal Blais
2013 arXiv   pre-print
Then, the second step is based on SIFT keypoints and uses the bag of words representation of the regions for the classification.  ...  This paper proposes a semantic segmentation method for outdoor scenes captured by a surveillance camera.  ...  For region recognition tasks, several induction models use global image features, local visual features, or a combination of both.  ... 
arXiv:1305.3189v1 fatcat:rhxgqma4kfdediw374su5huhhm

Condition Monitor System for Rotation Machine by CNN with Recurrence Plot

Hsueh, Ittangihala, Wu, Chang, Kuo
2019 Energies  
In this paper, we introduce an effective framework for fault diagnosis of 3-phase induction motors. The proposed framework mainly consists of two parts.  ...  The generated RP images are considered as input for the proposed CNN in the texture image recognition task.  ...  The relevant data were collected for a total of five conditions of the induction motor.  ... 
doi:10.3390/en12173221 fatcat:yrjwkjzmuzcd5iktfb3hzanmfe

Operational Automatic Remote Sensing Image Understanding Systems: Beyond Geographic Object-Based and Object-Oriented Image Analysis (GEOBIA/GEOOIA). Part 1: Introduction

Andrea Baraldi, Luigi Boschetti
2012 Remote Sensing  
To outperform the degree of automation, accuracy, efficiency, robustness, scalability and timeliness of existing GEOBIA/GEOOIA systems in compliance with the Quality Assurance Framework for Earth Observation  ...  (RS-IUS), from sub-symbolic statistical model-based (inductive) image segmentation to symbolic physical model-based (deductive) image preliminary classification.  ...  Capurro for his hospitality, patience, politeness and open-mindedness.  ... 
doi:10.3390/rs4092694 fatcat:cm7khgyvhrhevbu46wnkf2pcfu

Unsupervised Learning by Program Synthesis

Kevin Ellis, Armando Solar-Lezama, Joshua B. Tenenbaum
2015 Neural Information Processing Systems  
Taken together, these results give both a new approach to unsupervised learning of symbolic compositional structures, and a technique for applying program synthesis tools to noisy data.  ...  We apply our techniques to both a visual learning domain and a language learning problem, showing that our algorithm can learn many visual concepts from only a few examples and that it can recover some  ...  Acknowledgments We are grateful for discussions with Timothy O'Donnell on morphological rule learners, for advice from Brendan Lake and Tejas Kulkarni on the convolutional network baselines, and for the  ... 
dblp:conf/nips/EllisST15 fatcat:rdrn54skcfhxncjwqgiouxylui

A Framework for Medical Images Classification Using Soft Set

Saima Anwar Lashari, Rosziati Ibrahim
2013 Procedia Technology - Elsevier  
As a result, a new framework for medical imaging classification consisting of six phases namely: data acquisition, data pre-processing, data partition, soft set classifier, data analysis and performance  ...  For these several medical imaging modalities and applications based on data mining techniques have been proposed and developed.  ...  Acknowledgements The authors would like to thank Ministry of Higher Education (MOHE) and Universiti Tun Hussein Onn Malaysia (UTHM) for supporting this research under the Fundamental Research Grant Scheme  ... 
doi:10.1016/j.protcy.2013.12.227 fatcat:opxhly23ivdzjksqk7gylnde3q
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