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
More filters
Posted Content
Tokens-to-Token ViT: Training Vision Transformers from Scratch on ImageNet
Li Yuan,Yunpeng Chen,Tao Wang,Weihao Yu,Yujun Shi,Zihang Jiang,Francis Eh Tay,Jiashi Feng,Shuicheng Yan +8 more
TL;DR: T2T-ViT as mentioned in this paper proposes a token-to-token transformation to progressively transform the image to tokens by recursively aggregating neighboring tokens into one token (Token-To-Token), such that local structure represented by surrounding tokens can be modeled and tokens length can be reduced.
Proceedings ArticleDOI
Everybody Dance Now
TL;DR: This paper presents a simple method for “do as I do” motion transfer: given a source video of a person dancing, it is shown that it can transfer that performance to a novel (amateur) target after only a few minutes of the target subject performing standard moves.
Proceedings ArticleDOI
Fast and Furious: Real Time End-to-End 3D Detection, Tracking and Motion Forecasting with a Single Convolutional Net
TL;DR: A novel deep neural network that is able to jointly reason about 3D detection, tracking and motion forecasting given data captured by a 3D sensor is proposed, which is very efficient in terms of both memory and computation.
Book ChapterDOI
Graph R-CNN for Scene Graph Generation
TL;DR: A novel scene graph generation model called Graph R-CNN, that is both effective and efficient at detecting objects and their relations in images, is proposed and a new evaluation metric is introduced that is more holistic and realistic than existing metrics.
Journal ArticleDOI
Convolutional Neural Network With Data Augmentation for SAR Target Recognition
TL;DR: The capability of a deep convolutional neural network (CNN) combined with three types of data augmentation operations in SAR target recognition is investigated, showing that it is a practical approach for target recognition in challenging conditions of target translation, random speckle noise, and missing pose.
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.