• Corpus ID: 174799378

Compact Approximation for Polynomial of Covariance Feature

@article{Mukuta2019CompactAF,
  title={Compact Approximation for Polynomial of Covariance Feature},
  author={Yusuke Mukuta and Tatsuaki Machida and Tatsuya Harada},
  journal={ArXiv},
  year={2019},
  volume={abs/1906.01851}
}
Covariance pooling is a feature pooling method with good classification accuracy. Because covariance features consist of second-order statistics, the scale of the feature elements are varied. Therefore, normalizing covariance features using a matrix square root affects the performance improvement. When pooling methods are applied to local features extracted from CNN models, the accuracy increases when the pooling function is back-propagatable and the feature-extraction model is learned in an… 

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References

SHOWING 1-10 OF 19 REFERENCES
Improved Bilinear Pooling with CNNs
TLDR
This paper investigates various ways of normalizing second-order statistics of convolutional features to improve their representation power and finds that the matrix square-root normalization offers significant improvements and outperforms alternative schemes such as the matrix logarithm normalization when combined with elementwisesquare-root and l2 normalization.
Is Second-Order Information Helpful for Large-Scale Visual Recognition?
TLDR
A Matrix Power Normalized Covariance (MPNCOV) method that develops forward and backward propagation formulas regarding the nonlinear matrix functions such that MPN-COV can be trained end-to-end and analyzes both qualitatively and quantitatively its advantage over the well-known Log-Euclidean metric.
Towards Faster Training of Global Covariance Pooling Networks by Iterative Matrix Square Root Normalization
TLDR
This work proposes an iterative matrix square root normalization method for fast end-to-end training of global covariance pooling networks, which is much faster than EIG or SVD based methods, since it involves only matrix multiplications, suitable for parallel implementation on GPU.
MoNet: Moments Embedding Network
TLDR
This paper unify bilinear pooling and the global Gaussian embedding layers through the empirical moment matrix and proposes a novel sub-matrix square-root layer, which can be used to normalize the output of the convolution layer directly and mitigate the dimensionality problem with off-the-shelf compact pooling methods.
Bilinear CNN Models for Fine-Grained Visual Recognition
We propose bilinear models, a recognition architecture that consists of two feature extractors whose outputs are multiplied using outer product at each location of the image and pooled to obtain an
Compact Bilinear Pooling
TLDR
Two compact bilinear representations are proposed with the same discriminative power as the full bil inear representation but with only a few thousand dimensions allowing back-propagation of classification errors enabling an end-to-end optimization of the visual recognition system.
Matrix Backpropagation for Deep Networks with Structured Layers
TLDR
A sound mathematical apparatus to formally integrate global structured computation into deep computation architectures and demonstrates that deep networks relying on second-order pooling and normalized cuts layers, trained end-to-end using matrix backpropagation, outperform counterparts that do not take advantage of such global layers.
G2DeNet: Global Gaussian Distribution Embedding Network and Its Application to Visual Recognition
TLDR
Experimental results on large scale region classification and fine-grained recognition tasks show that G2DeNet is superior to its counterparts, capable of achieving state-of-the-art performance.
Very Deep Convolutional Networks for Large-Scale Image Recognition
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.
Fast and scalable polynomial kernels via explicit feature maps
TLDR
A novel randomized tensor product technique, called Tensor Sketching, is proposed for approximating any polynomial kernel in O(n(d+D \log{D})) time, and achieves higher accuracy and often runs orders of magnitude faster than the state-of-the-art approach for large-scale real-world datasets.
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