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
Journal ArticleDOI
REFUGE Challenge: A unified framework for evaluating automated methods for glaucoma assessment from fundus photographs
José Ignacio Orlando,Huazhu Fu,João Barbossa Breda,Karel Van Keer,Deepti R. Bathula,Andres Diaz-Pinto,Ruogu Fang,Pheng-Ann Heng,Jeyoung Kim,Joon-Ho Lee,Joonseok Lee,Xiaoxiao Li,Peng Liu,Shuai Lu,Balamurali Murugesan,Valery Naranjo,Sai Samarth R. Phaye,Sharath M Shankaranarayana,Apoorva Sikka,Jaemin Son,Anton van den Hengel,Shujun Wang,Junyan Wu,Zifeng Wu,Guanghui Xu,Yongli Xu,Pengshuai Yin,Fei Li,Xiulan Zhang,Yanwu Xu,Hrvoje Bogunovic +30 more
TL;DR: It is observed that two of the top-ranked teams outperformed two human experts in the glaucoma classification task, and the segmentation results were in general consistent with the ground truth annotations, with complementary outcomes that can be further exploited by ensembling the results.
Journal ArticleDOI
End-to-End Blind Image Quality Assessment Using Deep Neural Networks
TL;DR: This work demonstrates the strong competitiveness of MEON against state-of-the-art BIQA models using the group maximum differentiation competition methodology and empirically demonstrates that GDN is effective at reducing model parameters/layers while achieving similar quality prediction performance.
Journal ArticleDOI
Learning Two-Branch Neural Networks for Image-Text Matching Tasks
TL;DR: In this article, a two-branch neural network is proposed to learn the similarity between image-sentence matching and visual grounding, which achieves high accuracies for phrase localization on the Flickr30K Entities dataset and for bi-directional imagesentence retrieval on Flickr30k and MSCOCO datasets.
Proceedings ArticleDOI
YOLO-LITE: A Real-Time Object Detection Algorithm Optimized for Non-GPU Computers
TL;DR: YOLO-LITE as discussed by the authors is a real-time object detection model developed to run on portable devices such as a laptop or cellphone lacking a Graphics Processing Unit (GPU) and achieves a mAP of 33.81% and 12.26% respectively.
Posted Content
CDC: Convolutional-De-Convolutional Networks for Precise Temporal Action Localization in Untrimmed Videos
TL;DR: A novel Convolutional-De-Convolutional (CDC) network that places CDC filters on top of 3D ConvNets, which have been shown to be effective for abstracting action semantics but reduce the temporal length of the input data.
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.