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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TL;DR: This paper mainly focus on the application of deep learning architectures to three major applications, namely (i) wild animal detection, (ii) small arm detection and (iii) human being detection.
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Emerging artificial intelligence methods in structural engineering
Hadi Salehi,Rigoberto Burgueño +1 more
TL;DR: Techniques concerning applications of the noted AI methods in structural engineering developed over the last decade are summarized.
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R2CNN: Rotational Region CNN for Orientation Robust Scene Text Detection.
TL;DR: A novel method called Rotational Region CNN (R2CNN) for detecting arbitrary-oriented texts in natural scene images using the Region Proposal Network to generate axis-aligned bounding boxes that enclose the texts with different orientations.
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FOTS: Fast Oriented Text Spotting with a Unified Network
TL;DR: In this article, a unified end-to-end trainable Fast Oriented Text Spotting (FOTS) network is proposed for simultaneous detection and recognition, sharing computation and visual information among the two complementary tasks.
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Look and Think Twice: Capturing Top-Down Visual Attention with Feedback Convolutional Neural Networks
Chunshui Cao,Xianming Liu,Yi Yang,Yinan Yu,Jiang Wang,Zilei Wang,Yongzhen Huang,Liang Wang,Chang Huang,Wei Xu,Deva Ramanan,Thomas S. Huang +11 more
TL;DR: The background of feedbacks in the human visual cortex is introduced, which motivates the development of a computational feedback mechanism in deep neural networks, and a feedback loop is introduced to infer the activation status of hidden layer neurons according to the "goal" of the network.
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.