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
Central focused convolutional neural networks: Developing a data-driven model for lung nodule segmentation
Shuo Wang,Mu Zhou,Zaiyi Liu,Zhenyu Liu,Dongsheng Gu,Yali Zang,Di Dong,Olivier Gevaert,Jie Tian +8 more
TL;DR: The proposed data‐driven model, termed the Central Focused Convolutional Neural Networks (CF‐CNN), to segment lung nodules from heterogeneous CT images achieved superior segmentation performance with average dice scores of 82.15% and 80.02% for the two datasets respectively.
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Multimodal Compact Bilinear Pooling for Visual Question Answering and Visual Grounding
TL;DR: This paper proposed Multimodal Compact Bilinear Pooling (MCB) to efficiently and expressively combine multimodal features for visual question answering and grounding tasks, which outperformed the state-of-the-art on the Visual7W dataset and the VQA challenge.
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Accelerating Binarized Convolutional Neural Networks with Software-Programmable FPGAs
Ritchie Zhao,Weinan Song,Wentao Zhang,Tianwei Xing,Jeng-Hau Lin,Mani Srivastava,Rajesh Gupta,Zhiru Zhang +7 more
TL;DR: The design of a BNN accelerator is presented that is synthesized from C++ to FPGA-targeted Verilog and outperforms existing FPGAs-based CNN accelerators in GOPS as well as energy and resource efficiency.
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Handcrafted vs. non-handcrafted features for computer vision classification
TL;DR: A generic computer vision system designed for exploiting trained deep Convolutional Neural Networks as a generic feature extractor and mixing these features with more traditional hand-crafted features is presented, demonstrating the generalizability of the proposed approach.
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Differentiable Monte Carlo ray tracing through edge sampling
TL;DR: This work introduces a general-purpose differentiable ray tracer, which is the first comprehensive solution that is able to compute derivatives of scalar functions over a rendered image with respect to arbitrary scene parameters such as camera pose, scene geometry, materials, and lighting parameters.
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
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.