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

@article{Razavian2014CNNFO,
  title={CNN Features Off-the-Shelf: An Astounding Baseline for Recognition},
  author={Ali Sharif Razavian and Hossein Azizpour and Josephine Sullivan and Stefan Carlsson},
  journal={2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops},
  year={2014},
  pages={512-519}
}
Recent results indicate that the generic descriptors extracted from the convolutional neural networks are very powerful. This paper adds to the mounting evidence that this is indeed the case. We report on a series of experiments conducted for different recognition tasks using the publicly available code and model of the OverFeat network which was trained to perform object classification on ILSVRC13. We use features extracted from the OverFeat network as a generic image representation to tackle… CONTINUE READING

Citations

Publications citing this paper.
SHOWING 1-10 OF 2,023 CITATIONS, ESTIMATED 33% COVERAGE

Embedding Based on Function Approximation for Large Scale Image Search

  • IEEE Transactions on Pattern Analysis and Machine Intelligence
  • 2018
VIEW 11 EXCERPTS
CITES BACKGROUND & METHODS
HIGHLY INFLUENCED

Implementing Deep Learning and Inferencing on Fog and Edge Computing Systems

  • 2018 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)
  • 2018
VIEW 6 EXCERPTS
CITES METHODS & BACKGROUND
HIGHLY INFLUENCED

Indoor Visual Positioning Aided by CNN-Based Image Retrieval: Training-Free, 3D Modeling-Free

  • Sensors
  • 2018
VIEW 15 EXCERPTS
CITES METHODS & BACKGROUND
HIGHLY INFLUENCED

Context based image analysis with application in dietary assessment and evaluation

  • Multimedia Tools and Applications
  • 2017
VIEW 9 EXCERPTS
CITES METHODS & BACKGROUND
HIGHLY INFLUENCED

Visual Script and Language Identification

  • 2016 12th IAPR Workshop on Document Analysis Systems (DAS)
  • 2016
VIEW 6 EXCERPTS
CITES METHODS & BACKGROUND
HIGHLY INFLUENCED

The application of two-level attention models in deep convolutional neural network for fine-grained image classification

  • 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
  • 2015
VIEW 12 EXCERPTS
CITES BACKGROUND & METHODS
HIGHLY INFLUENCED

Automated Pruning for Deep Neural Network Compression

  • 2018 24th International Conference on Pattern Recognition (ICPR)
  • 2018
VIEW 5 EXCERPTS
CITES METHODS & BACKGROUND
HIGHLY INFLUENCED

FILTER CITATIONS BY YEAR

2013
2019

CITATION STATISTICS

  • 171 Highly Influenced Citations

  • Averaged 524 Citations per year over the last 3 years

  • 2% Increase in citations per year in 2018 over 2017

References

Publications referenced by this paper.
SHOWING 1-10 OF 42 REFERENCES

Contextualizing Object Detection and Classification

  • IEEE Trans. Pattern Anal. Mach. Intell.
  • 2015
VIEW 14 EXCERPTS
HIGHLY INFLUENTIAL

All About VLAD

  • 2013 IEEE Conference on Computer Vision and Pattern Recognition
  • 2013
VIEW 4 EXCERPTS
HIGHLY INFLUENTIAL

and A

M. Everingham, L. Van Gool, C.K.I. Williams, J. Winn
  • Zisserman. The PASCAL Visual Object Classes Challenge 2012
  • 2012
VIEW 14 EXCERPTS
HIGHLY INFLUENTIAL

Deformable Part Descriptors for Fine-Grained Recognition and Attribute Prediction

  • 2013 IEEE International Conference on Computer Vision
  • 2013
VIEW 5 EXCERPTS
HIGHLY INFLUENTIAL

Large-scale image retrieval with compressed Fisher vectors

  • 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition
  • 2010
VIEW 4 EXCERPTS
HIGHLY INFLUENTIAL

Similar Papers

Loading similar papers…