Open AccessProceedings Article
Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan,Andrew Zisserman +1 more
Reads0
Chats0
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
More filters
Book ChapterDOI
Shift-Net: Image Inpainting via Deep Feature Rearrangement
TL;DR: A special shift-connection layer to the U-Net architecture, namely Shift-Net, is introduced for filling in missing regions of any shape with sharp structures and fine-detailed textures and an end-to-end learning algorithm is further developed to train the Shift- net.
Proceedings ArticleDOI
Convolutional Feature Masking for Joint Object and Stuff Segmentation
Jifeng Dai,Kaiming He,Jian Sun +2 more
TL;DR: This paper proposes a joint method to handle objects and “stuff” (e.g., grass, sky, water) in the same framework and presents state-of-the-art results on benchmarks of PASCAL VOC and new PASCal-CONTEXT.
Journal ArticleDOI
Improving Computer-aided Detection using Convolutional Neural Networks and Random View Aggregation
Holger R. Roth,Le Lu,Jiamin Liu,Jianhua Yao,Ari Seff,Kevin M. Cherry,Lauren Kim,Ronald M. Summers +7 more
TL;DR: Experimental results show the ability of ConvNets to generalize well to different medical imaging CADe applications and scale elegantly to various data sets.
Proceedings ArticleDOI
Scalpel: Customizing DNN Pruning to the Underlying Hardware Parallelism
TL;DR: This work implemented weight pruning for several popular networks on a variety of hardware platforms and observed surprising results, including mean speedups of 3.54x, 2.61x, and 1.25x while reducing the model sizes by 88, 82%, and 53%.
Posted Content
Flickr30k Entities: Collecting Region-to-Phrase Correspondences for Richer Image-to-Sentence Models
Bryan A. Plummer,Liwei Wang,Christopher M. Cervantes,Juan C. Caicedo,Julia Hockenmaier,Svetlana Lazebnik +5 more
TL;DR: This paper presents Flickr30K Entities, which augments the 158k captions from Flickr30k with 244k coreference chains, linking mentions of the same entities across different captions for the same image, and associating them with 276k manually annotated bounding boxes.
References
More filters
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.