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Prototypical Priors: From Improving Classification to Zero-Shot Learning
[article]
2018
arXiv
pre-print
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In zero-shot learning scenarios, the same system can be directly deployed to draw inference on unseen classes by simply adding the prototypical information for these new classes at test time. ...
Using prototypes as prior information, the deepnet pipeline learns the input image projections into the prototypical embedding space subject to minimization of the final classification loss. ...
The first stage of our network consists of a CNN to enable learning of image features starting from original RGB patches of 48 × 48 (size suitable for both traffic-sign and logo samples in experimental ...
arXiv:1512.01192v2
fatcat:6r7lo54tibhwropgayzm3iqe5e
Prototypical Priors: From Improving Classification to Zero-Shot Learning
2015
Procedings of the British Machine Vision Conference 2015
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In zero-shot learning scenarios, the same system can be directly deployed to draw inference on unseen classes by simply adding the prototypical information for these new classes at test time. ...
Using prototypes as prior information, the deepnet pipeline learns the input image projections into the prototypical embedding space subject to minimization of the final classification loss. ...
The first stage of our network consists of a CNN to enable learning of image features starting from original RGB patches of 48 × 48 (size suitable for both traffic-sign and logo samples in experimental ...
doi:10.5244/c.29.120
dblp:conf/bmvc/JetleyRJT15
fatcat:aph7jcjonnhv3br7n7remq6cli
Deep Metric Learning to Rank
2019
2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
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On three few-shot image retrieval datasets, FastAP consistently outperforms competing methods, which often involve complex optimization heuristics or costly model ensembles. − ...
We propose a novel deep metric learning method by revisiting the learning to rank approach. ...
, feature matching [14] , and fewshot learning [38] . ...
doi:10.1109/cvpr.2019.00196
dblp:conf/cvpr/Cakir0XKS19
fatcat:dp7zjz36ovhtlgqhe4zgjchvhm
Data-driven geophysics: from dictionary learning to deep learning
[article]
2020
arXiv
pre-print
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We present a coding tutorial and a summary of tips for beginners and interested geophysical readers to rapidly explore deep learning. ...
Some promising directions are provided for future research involving deep learning in geophysics, such as unsupervised learning, transfer learning, multimodal deep learning, federated learning, uncertainty ...
Acknowledgments The work was supported in part by the National Key Research and Development Program
Data Availability Statement Data were not used, nor created for this research. ...
arXiv:2007.06183v2
fatcat:ow45ejo7izbkpmssedwd74rbym
Learning to Group: A Bottom-Up Framework for 3D Part Discovery in Unseen Categories
[article]
2021
arXiv
pre-print
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At the core of our approach is to restrict the local context for extracting part-level features, which encourages the generalizability to unseen categories. ...
Being able to learn the geometry prior of parts and transfer this prior to unseen categories pose fundamental challenges on data-driven shape segmentation approaches. ...
Here also exists a cascaded structure where the verification network will focus on the pairs selected by the policy network and make a double verification. ...
arXiv:2002.06478v4
fatcat:knms24hbdbeilmk24xdy27lxlu
A Comprehensive Survey of Machine Learning Applied to Radar Signal Processing
[article]
2020
arXiv
pre-print
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With the rapid development of machine learning (ML), especially deep learning, radar researchers have started integrating these new methods when solving RSP-related problems. ...
This paper aims at helping researchers and practitioners to better understand the application of ML techniques to RSP-related problems by providing a comprehensive, structured and reasoned literature overview ...
Few-shot learning can rapidly generalize to new tasks of limited supervised experience by turning to prior knowledge, which mimics human's ability to acquire knowledge from few examples through generalization ...
arXiv:2009.13702v1
fatcat:m6am73324zdwba736sn3vmph3i
Machine Learning Approach to Detect Drowsiness on Behavioral Parameters
2022
Ymer
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Our project is focused on building a single-access platform for various object detection tasks. ...
Thanks to the availability of large amounts of data, faster GPUs, and improved algorithms, we can now quickly train computers to detect and classify many elements inside a picture with high accuracy. ...
The method focuses on multiple subregions of images (grayscale images) and works on recognition of faces by looking for certain attributes in each subregion. ...
doi:10.37896/ymer21.01/01
fatcat:rdmpkrttdngf7gijqqhwcdz7uu
A Survey on Text Classification: From Shallow to Deep Learning
[article]
2021
arXiv
pre-print
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We create a taxonomy for text classification according to the text involved and the models used for feature extraction and classification. ...
This paper fills the gap by reviewing the state-of-the-art approaches from 1961 to 2021, focusing on models from traditional models to deep learning. ...
Thanks for computing infrastructure provided by Huawei MindSpore platform. ...
arXiv:2008.00364v6
fatcat:a6zp52rtf5awlh253yp62wqt3a
Learning to Recognize Faces by Successive Meetings
2006
Journal of Multimedia
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In this paper we focus on the face recognition problem. ...
However, instead of following the usual approach of manually gathering and registering face images to build a training set to compute a classifier off-line, the system will start with an empty training ...
A simple way to implement that using recognition and/or verification techniques is to apply both approaches in a cascade configuration. ...
doi:10.4304/jmm.1.7.1-8
fatcat:ui4xsq7t5vanhp4ac3a5i22bli
A Deep Learning based Light-weight Face Mask Detector with Residual Context Attention and Gaussian Heatmap to Fight Against COVID-19
2021
IEEE Access
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To cope with this problem, we propose two novel modules -RCAM, to focus on learning important information, in section III-B, and SGHR, to learn more discriminating features for faces with and without masks ...
To focus on the important face mask related features, we cascade a convolutional block attention module (CBAM) [42] after the CEB, and add a skip connection. ...
doi:10.1109/access.2021.3095191
fatcat:yma42liatzgdnj6wlbnl3ulnri
Personalized Fashion Recommendation from Personal Social Media Data: An Item-to-Set Metric Learning Approach
[article]
2020
arXiv
pre-print
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Meanwhile, social networks provide an open and new data source for personalized fashion analysis. ...
To this end, we present an item-to-set metric learning framework that learns to compute the similarity between a set of historical fashion items of a user to a new fashion item. ...
The feature averaging operation is also proposed in [33] for few-shot learning. ...
arXiv:2005.12439v1
fatcat:xy3gsq4hrbg2jlt74b2nrqdbba
Glance and Focus: a Dynamic Approach to Reducing Spatial Redundancy in Image Classification
[article]
2020
arXiv
pre-print
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are strategically selected from the original image with reinforcement learning. ...
The accuracy of deep convolutional neural networks (CNNs) generally improves when fueled with high resolution images. However, this often comes at a high computational cost and high memory footprint. ...
Very deep convolutional networks for large-scale
image recognition. ...
arXiv:2010.05300v1
fatcat:7gwawa2usbcttbqlyex6xybbke
Learning to Reconstruct Shapes from Unseen Classes
[article]
2018
arXiv
pre-print
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From a single image, humans are able to perceive the full 3D shape of an object by exploiting learned shape priors from everyday life. ...
surfaces, and 3D voxel-based representations, in a principled manner that exploits the causal structure of how 3D shapes give rise to 2D images. ...
Acknowledgements We thank the anonymous reviewers for their constructive comments. ...
arXiv:1812.11166v1
fatcat:jd6l3xva3nbglmh7l73miqgxoi
Deep learning in radiology: An overview of the concepts and a survey of the state of the art with focus on MRI
2018
Journal of Magnetic Resonance Imaging
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Deep-learning algorithms have shown groundbreaking performance in a variety of sophisticated tasks, especially those related to images. They have often matched or exceeded human performance. ...
Deep learning is a branch of artificial intelligence where networks of simple interconnected units are used to extract patterns from data in order to solve complex problems. ...
Acknowledgments: The authors would like to thank Gemini Janas for reviewing and editing this article. ...
doi:10.1002/jmri.26534
pmid:30575178
pmcid:PMC6483404
fatcat:7jg5sr7z6bbehd6xabsjw6bcde
Review of end-to-end speech synthesis technology based on deep learning
[article]
2021
arXiv
pre-print
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Due to the limitations of high complexity and low efficiency of traditional speech synthesis technology, the current research focus is the deep learning-based end-to-end speech synthesis technology, which ...
Moreover, this paper also summarizes the open-source speech corpus of English, Chinese and other languages that can be used for speech synthesis tasks, and introduces some commonly used subjective and ...
For example, in the SPSS model based on deep neural network (DNN), DNN can learn the mapping function from linguistic features (input) to acoustic features (output). ...
arXiv:2104.09995v1
fatcat:q5lx74ycx5hobjox4ktl3amfta
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