Open AccessProceedings Article
Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan,Andrew Zisserman +1 more
TLDR
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
Citations
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Journal ArticleDOI
MedGAN: Medical image translation using GANs
Karim Armanious,Karim Armanious,Chenming Jiang,Marc Fischer,Thomas Küstner,Tobias Hepp,Konstantin Nikolaou,Sergios Gatidis,Bin Yang +8 more
TL;DR: A new framework, named MedGAN, is proposed for medical image-to-image translation which operates on the image level in an end- to-end manner and outperforms other existing translation approaches.
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Video Paragraph Captioning Using Hierarchical Recurrent Neural Networks
TL;DR: An approach that exploits hierarchical Recurrent Neural Networks to tackle the video captioning problem, i.e., generating one or multiple sentences to describe a realistic video, significantly outperforms the current state-of-the-art methods.
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Sequence to Sequence -- Video to Text
Subhashini Venugopalan,Marcus Rohrbach,Jeff Donahue,Raymond J. Mooney,Trevor Darrell,Kate Saenko +5 more
TL;DR: A novel end- to-end sequence-to-sequence model to generate captions for videos that naturally is able to learn the temporal structure of the sequence of frames as well as the sequence model of the generated sentences, i.e. a language model.
Journal ArticleDOI
Trunk-Branch Ensemble Convolutional Neural Networks for Video-Based Face Recognition
Changxing Ding,Dacheng Tao +1 more
TL;DR: A Trunk-Branch Ensemble CNN model (TBE-CNN), which extracts complementary information from holistic face images and patches cropped around facial components, achieves state-of-the-art performance on three popular video face databases: PaSC, COX Face, and YouTube Faces.
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A-Fast-RCNN: Hard Positive Generation via Adversary for Object Detection
TL;DR: This paper proposes to learn an adversarial network that generates examples with occlusions and deformations, the goal of the adversary is to generate examples that are difficult for the object detector to classify and both the original detector and adversary are learned in a joint manner.
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.
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Fully Convolutional Networks for Semantic Segmentation
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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.