Open AccessProceedings Article
Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan,Andrew Zisserman +1 more
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This work investigates the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting using an architecture with very small convolution filters, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 weight layers.Abstract:
In this work we investigate the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting. Our main contribution is a thorough evaluation of networks of increasing depth using an architecture with very small (3x3) convolution filters, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 weight layers. These findings were the basis of our ImageNet Challenge 2014 submission, where our team secured the first and the second places in the localisation and classification tracks respectively. We also show that our representations generalise well to other datasets, where they achieve state-of-the-art results. We have made our two best-performing ConvNet models publicly available to facilitate further research on the use of deep visual representations in computer vision.read more
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ESPnet: End-to-End Speech Processing Toolkit
Shinji Watanabe,Takaaki Hori,Shigeki Karita,Tomoki Hayashi,Jiro Nishitoba,Yuya Unno,Nelson Yalta,Jahn Heymann,Matthew Wiesner,Nanxin Chen,Adithya Renduchintala,Tsubasa Ochiai +11 more
TL;DR: A major architecture of this software platform, several important functionalities, which differentiate ESPnet from other open source ASR toolkits, and experimental results with major ASR benchmarks are explained.
Proceedings ArticleDOI
Visual Translation Embedding Network for Visual Relation Detection
TL;DR: Zhang et al. as discussed by the authors proposed a Visual Translation Embedding Network (VTransE) for visual relation detection, which places objects in a low-dimensional relation space where a relation can be modeled as a simple vector translation, i.e., subject + predicate.
Journal ArticleDOI
Deep Learning Localizes and Identifies Polyps in Real Time With 96% Accuracy in Screening Colonoscopy.
Gregor Urban,Priyam V. Tripathi,Talal Alkayali,Mohit Mittal,Farid Jalali,William E. Karnes,Pierre Baldi +6 more
TL;DR: The ability of computer-assisted image analysis using convolutional neural networks (CNNs; a deep learning model for image analysis) to improve polyp detection, a surrogate of ADR, is tested and could increase the ADR and decrease interval colorectal cancers but requires validation in large multicenter trials.
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Anatomically Constrained Neural Networks (ACNN): Application to Cardiac Image Enhancement and Segmentation
Ozan Oktay,Enzo Ferrante,Konstantinos Kamnitsas,Mattias P. Heinrich,Wenjia Bai,Jose Caballero,Stuart A. Cook,Antonio de Marvao,Timothy J W Dawes,Declan P. O'Regan,Bernhard Kainz,Ben Glocker,Daniel Rueckert +12 more
TL;DR: This work proposes a generic training strategy that incorporates anatomical prior knowledge into CNNs through a new regularisation model, which is trained end-to-end and demonstrates how the learnt deep models of 3-D shapes can be interpreted and used as biomarkers for classification of cardiac pathologies.
Proceedings ArticleDOI
SemanticFusion: Dense 3D semantic mapping with convolutional neural networks
TL;DR: In this paper, the authors combine CNNs and a state-of-the-art dense SLAM system, ElasticFusion, which provides long-term dense correspondences between frames of indoor RGB-D video even during loopy scanning trajectories.
References
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Proceedings ArticleDOI
ImageNet: A large-scale hierarchical image database
TL;DR: A new database called “ImageNet” is introduced, a large-scale ontology of images built upon the backbone of the WordNet structure, much larger in scale and diversity and much more accurate than the current image datasets.
Proceedings ArticleDOI
Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation
TL;DR: RCNN as discussed by the authors combines CNNs with bottom-up region proposals to localize and segment objects, and when labeled training data is scarce, supervised pre-training for an auxiliary task, followed by domain-specific fine-tuning, yields a significant performance boost.
Posted Content
Fully Convolutional Networks for Semantic Segmentation
TL;DR: It is shown that convolutional networks by themselves, trained end- to-end, pixels-to-pixels, improve on the previous best result in semantic segmentation.
Journal ArticleDOI
Backpropagation applied to handwritten zip code recognition
Yann LeCun,Bernhard E. Boser,John S. Denker,D. Henderson,Richard Howard,W. Hubbard,Lawrence D. Jackel +6 more
TL;DR: This paper demonstrates how constraints from the task domain can be integrated into a backpropagation network through the architecture of the network, successfully applied to the recognition of handwritten zip code digits provided by the U.S. Postal Service.
Journal ArticleDOI
The Pascal Visual Object Classes Challenge: A Retrospective
TL;DR: A review of the Pascal Visual Object Classes challenge from 2008-2012 and an appraisal of the aspects of the challenge that worked well, and those that could be improved in future challenges.