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Front Matter: Volume 11878

Xudong Jiang, Hiroshi Fujita
2021 Thirteenth International Conference on Digital Image Processing (ICDIP 2021)  
using a Base 36 numbering system employing both numerals and letters.  ...  Utilization of CIDs allows articles to be fully citable as soon as they are published online, and connects the same identifier to all online and print versions of the publication.  ...  graphical classification 11878 0W Task-relevant graph to propagate labels for few-shot classification IMAGE DENOISING AND DIGITAL WATERMARKING 0Z Convolutional neural network combined with wavelet  ... 
doi:10.1117/12.2603859 fatcat:7iznff73tze2fdyoz2qy6ookpu

Few-Shot Learning with a Novel Voronoi Tessellation-Based Image Augmentation Method for Facial Palsy Detection

Olusola Oluwakemi Abayomi-Alli, Robertas Damaševičius, Rytis Maskeliūnas, Sanjay Misra
2021 Electronics  
The proposed method augments the image dataset with new images, which are used to train the deep neural network.  ...  We achieved an accuracy of 99.34% using two-shot learning with VDRRE augmentation on palsy faces from Youtube Face Palsy (YFP) dataset, while normal faces are taken from Caltech Face Database.  ...  Meta-learning methods [40, 41] tackle the few-shot learning problem by training neural networks to learn novel classes.  ... 
doi:10.3390/electronics10080978 fatcat:hxakucmtsffrxkitak3hg7id4q

Going Deeper Into Face Detection: A Survey [article]

Shervin Minaee, Ping Luo, Zhe Lin, Kevin Bowyer
2021 arXiv   pre-print
Face detection is a crucial first step in many facial recognition and face analysis systems.  ...  With the breakthrough work in image classification using deep neural networks in 2012, there has been a huge paradigm shift in face detection.  ...  ACKNOWLEDGMENTS The authors would like to thank Aleksei Stoliar for his comments and suggestions regarding this work.  ... 
arXiv:2103.14983v2 fatcat:3pdac7jpvzegdnz7qzqdrs3vx4

Optimizing Few-Shot Learning Based on Variational Autoencoders

Ruoqi Wei, Ausif Mahmood
2021 Entropy  
The purpose of our research is to increase the size of the training dataset using various methods to improve the accuracy and robustness of the few-shot face recognition.  ...  In this research, we suggest employing a generative approach using variational autoencoders (VAEs), which can be used specifically to optimize few-shot learning tasks by generating new samples with more  ...  We hope to use this method and transfer learning as the backend to achieve higher accuracy of few-shot face recognition.  ... 
doi:10.3390/e23111390 pmid:34828088 pmcid:PMC8618453 fatcat:b4433alqq5aujoo6yzcj5kz7bu

2021 Index IEEE Transactions on Multimedia Vol. 23

2021 IEEE transactions on multimedia  
The primary entry includes the coauthors' names, the title of the paper or other item, and its location, specified by the publication abbreviation, year, month, and inclusive pagination.  ...  The Subject Index contains entries describing the item under all appropriate subject headings, plus the first author's name, the publication abbreviation, month, and year, and inclusive pages.  ...  ., +, TMM 2021 4220-4231 Learning Dual-Pooling Graph Neural Networks for Few-Shot Video Classi-fication.  ... 
doi:10.1109/tmm.2022.3141947 fatcat:lil2nf3vd5ehbfgtslulu7y3lq

Recognition of Facial Expression using Deep Learning Model

Arunkumar S
2020 Figshare  
This work talks about the research works done in the field of facial expression identification and recognition method using Convolutional Neural Network (CNN).  ...  People are using social media to express their Expressions. Automatic Facial expression recognition is an emerging topic for researchers.  ...  Haar-cascade Classifier Convolutional Neural Network "Convolutional neural system (CNN) as appeared in Fig.2 might be an extraordinary kind of feedforward neural system initially utilized inside the  ... 
doi:10.6084/m9.figshare.12678401 fatcat:dezwfuorvzftjoyunzhau7toxi

Cross-domain Few-shot Micro-expression Recognition incorporating Action Units

Yi Dai, Ling Feng
2021 IEEE Access  
A Graph Convolutional Network (GCN) was then used to process AU node features and provide information for microexpression recognition.  ...  Since few-shot learning methods tend to use knowledge from other domains as supplementary knowledge, few-shot learning and cross-domain learning tasks are highly correlated and should be considered together  ... 
doi:10.1109/access.2021.3120542 fatcat:o3dpaqnxabao7n4yvlczw7z5by

Omni-supervised Facial Expression Recognition via Distilled Data [article]

Ping Liu, Yunchao Wei, Zibo Meng, Weihong Deng, Joey Tianyi Zhou, Yi Yang
2021 arXiv   pre-print
Facial expression plays an important role in understanding human emotions. Most recently, deep learning based methods have shown promising for facial expression recognition.  ...  From a different perspective, we propose to perform omni-supervised learning to directly exploit reliable samples in a large amount of unlabeled data for network training.  ...  Chan, and M. H. Mahoor, “Going deeper in facial expression recognition using deep neural networks,” in IEEE [45] I. Radosavovic, P. Dollár, R. Girshick, G. Gkioxari, and K.  ... 
arXiv:2005.08551v5 fatcat:37lk5obe3vf7tigejcssl3gow4

General Facial Representation Learning in a Visual-Linguistic Manner [article]

Yinglin Zheng, Hao Yang, Ting Zhang, Jianmin Bao, Dongdong Chen, Yangyu Huang, Lu Yuan, Dong Chen, Ming Zeng, Fang Wen
2021 arXiv   pre-print
In this paper, we study the transfer performance of pre-trained models on face analysis tasks and introduce a framework, called FaRL, for general Facial Representation Learning in a visual-linguistic manner  ...  How to learn a universal facial representation that boosts all face analysis tasks? This paper takes one step toward this goal.  ...  Momentum contrast for unsupervised visual rep- Deep alignment network: A convolutional neural network resentation learning.  ... 
arXiv:2112.03109v1 fatcat:rsialmokandv3lcruoeizr4wb4

Revisiting Few-Shot Learning for Facial Expression Recognition [article]

Anca-Nicoleta Ciubotaru, Arnout Devos, Behzad Bozorgtabar, Jean-Philippe Thiran, Maria Gabrani
2019 arXiv   pre-print
In this paper, we revisit and compare existing few-shot learning methods for the low-shot facial expression recognition in terms of their generalisation ability via episode-training.  ...  However, in the standard setting over-parameterised neural networks are not amenable for learning from few samples as they can quickly over-fit.  ...  ACKNOWLEDGEMENT This project is partially supported by the European Union's Horizon 2020 research and innovation program under the Marie Skodowska-Curie grant agreement No. 754354.  ... 
arXiv:1912.02751v2 fatcat:nfaifcpoxjcozhzrgxzd24mbmi

Learning Neural Textual Representations for Citation Recommendation

Binh Thanh Kieu, Inigo Jauregi Unanue, Son Bao Pham, Hieu Xuan Phan, Massimo Piccardi
2021 2020 25th International Conference on Pattern Recognition (ICPR)  
Few-Shot Learning and the Role of Spatial Attention DAY 1 -Jan 12, 2021 Castellano, Giovanna; Vessio, Gennaro 845 Deep Convolutional Embedding for Digitized Painting Clustering DAY 1 -Jan 12,  ...  the Meta-Learning Idea Able to Improve the Generalization of Deep Neural Networks on the Standard Supervised Learning?  ... 
doi:10.1109/icpr48806.2021.9412725 fatcat:3vge2tpd2zf7jcv5btcixnaikm

Enhancing Mouth-Based Emotion Recognition Using Transfer Learning

Valentina Franzoni, Giulio Biondi, Damiano Perri, Osvaldo Gervasi
2020 Sensors  
Using transfer learning, we can use fewer training data than training a whole network from scratch, and thus more efficiently fine-tune the network with emotional data and improve the convolutional neural  ...  This achievement takes advantage of previous preliminary works on mouth-based emotion recognition using deep-learning, and has the further benefit of having been tested and compared to a set of other networks  ...  We acknowledge the University of Denver, CO, USA, for having provided access to the database of images we used for the present study.  ... 
doi:10.3390/s20185222 pmid:32933178 fatcat:udk2e2jxvvarjby4xycrsxoz24

2020 Index IEEE Transactions on Image Processing Vol. 29

2020 IEEE Transactions on Image Processing  
Liu, J., +, TIP 2020 8028-8042 A Two-Stage Approach to Few-Shot Learning for Image Recognition. An Unordered Image Stitching Method Based on Binary Tree and Estimated Overlapping Area.  ...  ., +, Self-Enhanced Convolutional Network for Facial Video Hallucination. Fang, C., +, TIP 2020 3078-3090 Self-Supervised Learning of Detailed 3D Face Reconstruction.  ... 
doi:10.1109/tip.2020.3046056 fatcat:24m6k2elprf2nfmucbjzhvzk3m

2021 Index IEEE Transactions on Image Processing Vol. 30

2021 IEEE Transactions on Image Processing  
The primary entry includes the coauthors' names, the title of the paper or other item, and its location, specified by the publication abbreviation, year, month, and inclusive pagination.  ...  The Subject Index contains entries describing the item under all appropriate subject headings, plus the first author's name, the publication abbreviation, month, and year, and inclusive pages.  ...  ., +, TIP 2021 6544-6556 Learning Dynamic Relationships for Facial Expression Recognition Based on Graph Convolutional Network.  ... 
doi:10.1109/tip.2022.3142569 fatcat:z26yhwuecbgrnb2czhwjlf73qu

Table of Contents

2019 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)  
Bernal (missing) Three-Stream Convolutional Neural Network With Multi-Task and Ensemble Learning for 3D Action Recognition 934 Duohan Liang (missing), Guoliang Fan (missing), Guangfeng Lin (missing), Wanjun  ...  Sallee (missing) SAR Image Classification Using Few-Shot Cross-Domain Transfer Learning 907 Mohammad Rostami (missing), Soheil Kolouri (missing), Eric Eaton (missing), and Kyungnam Kim (missing) xiv Computer  ...  Learning Raw Image Denoising With Bayer Pattern Unification and Bayer Preserving Augmentation 2070 Jiaming Liu (missing) , Chi-Hao Wu (missing) , Yuzhi Wang (missing) , Qin Xu (missing), Yuqian Zhou  ... 
doi:10.1109/cvprw.2019.00004 fatcat:h7xpqwyrofdxniqtxbodn66mpy
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