Very Deep Convolutional Networks for Large-Scale Image Recognition

Published in

International Conference on Learning Representa...(2014)

External Links:

Generate Graph

TL;DR

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.

Authors

Andrew Zisserman

62 papers

K. Simonyan

25 papers

References43 items

Example Selection For Dictionary Learning

Actions and Attributes from Wholes and Parts

Deep visual-semantic alignments for generating image descriptions

Material recognition in the wild with the Materials in Context Database

Deep convolutional filter banks for texture recognition and segmentation

Very Deep Convolutional Networks for Large-Scale Image Recognition

Published in

International Conference on Learning Representa...(2014)

External Links:

Generate Graph

TL;DR

Abstract

Authors

Andrew Zisserman

62 papers

K. Simonyan

25 papers

References43 items

Example Selection For Dictionary Learning

Actions and Attributes from Wholes and Parts

Deep visual-semantic alignments for generating image descriptions

Material recognition in the wild with the Materials in Context Database

Deep convolutional filter banks for texture recognition and segmentation

Fully convolutional networks for semantic segmentation

Unifying Visual-Semantic Embeddings with Multimodal Neural Language Models

Going deeper with convolutions

ImageNet Large Scale Visual Recognition Challenge

The Pascal Visual Object Classes Challenge: A Retrospective

Learning and Transferring Mid-level Image Representations Using Convolutional Neural Networks

Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition

Two-Stream Convolutional Networks for Action Recognition in Videos

Return of the Devil in the Details: Delving Deep into Convolutional Nets

One weird trick for parallelizing convolutional neural networks

CNN Features Off-the-Shelf: An Astounding Baseline for Recognition

OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks

Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps

Exact solutions to the nonlinear dynamics of learning in deep linear neural networks

Multi-digit Number Recognition from Street View Imagery using Deep Convolutional Neural Networks

Some Improvements on Deep Convolutional Neural Network Based Image Classification

Network In Network

Visualizing and Understanding Convolutional Networks

Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation

DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition

ImageNet classification with deep convolutional neural networks

Large Scale Distributed Deep Networks

Multi-column deep neural networks for image classification

Flexible, High Performance Convolutional Neural Networks for Image Classification

Improving the Fisher Kernel for Large-Scale Image Classification

Understanding the difficulty of training deep feedforward neural networks

ImageNet: A large-scale hierarchical image database

Caltech-256 Object Category Dataset

Learning Generative Visual Models from Few Training Examples: An Incremental Bayesian Approach Tested on 101 Object Categories

Backpropagation Applied to Handwritten Zip Code Recognition

Regularized Max Pooling for Image Categorization

class-agnostic single-class regression (SCR), which differs from the findings

Single-label to multi-label. CoRR, abs/1406

Caffe: An open source convolutional architecture for fast feature embedding

Large scale visual recognition challenge (ILSVRC), 2010. URL http://www.image-net.org/challenges

International Journal of Computer Vision manuscript No. (will be inserted by the editor) The PASCAL Visual Object Classes (VOC) Challenge

Field of Study

Computer Science

Journal Information

Name

CoRR

Volume

abs/1409.1556

Venue Information

Name

International Conference on Learning Representations

Type

conference

URL

https://iclr.cc/

Alternate Names

Int Conf Learn Represent
ICLR

TL;DR

Abstract

Authors

References43 items

Example Selection For Dictionary Learning

Actions and Attributes from Wholes and Parts

Deep visual-semantic alignments for generating image descriptions

Material recognition in the wild with the Materials in Context Database

Deep convolutional filter banks for texture recognition and segmentation

TL;DR

Abstract

Authors

References43 items

Example Selection For Dictionary Learning

Actions and Attributes from Wholes and Parts

Deep visual-semantic alignments for generating image descriptions

Material recognition in the wild with the Materials in Context Database

Deep convolutional filter banks for texture recognition and segmentation

Fully convolutional networks for semantic segmentation

Unifying Visual-Semantic Embeddings with Multimodal Neural Language Models

Going deeper with convolutions

ImageNet Large Scale Visual Recognition Challenge

The Pascal Visual Object Classes Challenge: A Retrospective

Learning and Transferring Mid-level Image Representations Using Convolutional Neural Networks

Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition

Two-Stream Convolutional Networks for Action Recognition in Videos

Return of the Devil in the Details: Delving Deep into Convolutional Nets

One weird trick for parallelizing convolutional neural networks

CNN Features Off-the-Shelf: An Astounding Baseline for Recognition

OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks

Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps

Exact solutions to the nonlinear dynamics of learning in deep linear neural networks

Multi-digit Number Recognition from Street View Imagery using Deep Convolutional Neural Networks

Some Improvements on Deep Convolutional Neural Network Based Image Classification

Network In Network

Visualizing and Understanding Convolutional Networks

Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation

DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition

ImageNet classification with deep convolutional neural networks

Large Scale Distributed Deep Networks

Multi-column deep neural networks for image classification

Flexible, High Performance Convolutional Neural Networks for Image Classification

Improving the Fisher Kernel for Large-Scale Image Classification

Understanding the difficulty of training deep feedforward neural networks

ImageNet: A large-scale hierarchical image database

Caltech-256 Object Category Dataset

Learning Generative Visual Models from Few Training Examples: An Incremental Bayesian Approach Tested on 101 Object Categories

Backpropagation Applied to Handwritten Zip Code Recognition

A and V

Regularized Max Pooling for Image Categorization

class-agnostic single-class regression (SCR), which differs from the findings

Single-label to multi-label. CoRR, abs/1406

Caffe: An open source convolutional architecture for fast feature embedding

I and J

Large scale visual recognition challenge (ILSVRC), 2010. URL http://www.image-net.org/challenges

International Journal of Computer Vision manuscript No. (will be inserted by the editor) The PASCAL Visual Object Classes (VOC) Challenge

Field of Study

Journal Information

Name

Volume

Venue Information

Name

Type

URL

Alternate Names