This repositery proposed the implementation of several spatial poolings used for weakly supervised learning of deep ConvNets.
$ git clone https://github.com/durandtibo/spatial-pooling.torch.git
$ cd spatial-pooling.torch
$ luarocks make rocks/spatial-pooling-scm-1.rockspec
To test the installation, you can run
$ th test/test.lua
Global Max Pooling is a spatial pooling strategy used in "Is object localization for free? – Weakly Supervised Object Recognition with Convolutional Neural Networks ".
module = nn.GlobalMaxPooling()Applies 2D max-pooling operation on the whole image. The number of output features is equal to the number of input planes.
If the input image is a 4D tensor nBatchImage x nInputPlane x w x h, the output image size will be nBatchImage x nInputPlane x 1 x 1 where w and h are spatial image dimensions.
If the input image is a 3D tensor nInputPlane x w x h, the output image size will be nInputPlane x 1 x 1 where w and h are spatial image dimensions.
We note z^c the c-th map of input, and s^c the c-th map of the output.
s^c = max_{i,j} z^c_{i,j}
@inproceedings{Oquab_DeepMIL_CVPR15,
author = "Oquab, M. and Bottou, L. and Laptev, I. and Sivic, J.",
title = "Is object localization for free? – Weakly-supervised learning with convolutional neural networks",
booktitle = "Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition",
year = "2015"
}
GlobalkMaxPooling is a generalization of GlobalMaxPooling to multiple maximums. This spatial pooling strategy is inspired from Top Instances model "Multiple Instance Learning for Soft Bags via Top Instances".
module = nn.GlobalkMaxPooling(kMax)Applies 2D k-max-pooling operation on the whole image. The number of output features is equal to the number of input planes.
The parameter is the following:
kMax: The number of top instances.kMaxcan defined the number of selected regions (kMax >= 1) or the proportion of selected regions (0 < kMax < 1). IfkMax <= 0, all the regions are selected. Default iskMax = 1.
If the input image is a 4D tensor nBatchImage x nInputPlane x w x h, the output image size will be nBatchImage x nInputPlane x 1 x 1 where w and h are spatial image dimensions.
If the input image is a 3D tensor nInputPlane x w x h, the output image size will be nInputPlane x 1 x 1 where w and h are spatial image dimensions.
We note z^c the c-th map of input, and s^c the c-th map of the output.
s^c = max_{h in H_kMax} 1 / kMax sum_{i,j} h_{i,j} z^c_{i,j}
where H_k is such that h in H_k satisfies h_{i,j} in {0, 1} and sum_{i,j} h_{i,j} = k
- GlobalMaxPooling: if
kMax = 1 - GlobalAveragePooling: if
kMax = h x w
Global Max Pooling is a spatial pooling strategy used in "Learning Deep Features for Discriminative Localization ".
module = nn.GlobalAveragePooling()Applies 2D average-pooling operation on the whole image. The number of output features is equal to the number of input planes.
If the input image is a 4D tensor nBatchImage x nInputPlane x w x h, the output image size will be nBatchImage x nInputPlane x 1 x 1 where w and h are spatial image dimensions.
If the input image is a 3D tensor nInputPlane x w x h, the output image size will be nInputPlane x 1 x 1 where w and h are spatial image dimensions.
We note z^c the c-th map of input, and s^c the c-th map of the output.
s^c = 1 / (h * w) sum_{i,j} z^c_{i,j}
@inproceedings{Zhou_2016_CVPR,
author = {Zhou, Bolei and Khosla, Aditya and Lapedriza, Agata and Oliva, Aude and Torralba, Antonio},
title = {{Learning Deep Features for Discriminative Localization}},
booktitle = {CVPR},
year = {2016}
}
LogSumExpPooling is a spatial pooling strategy used in "From Image-level to Pixel-level Labeling with Convolutional Networks ".
module = nn.LogSumExpPooling(beta)Applies 2D LogSumExp-pooling operation on the whole image. The number of output features is equal to the number of input planes.
The parameter is the following:
beta: The anti-temperature parameter. Default isbeta=1.
If the input image is a 4D tensor nBatchImage x nInputPlane x w x h, the output image size will be nBatchImage x nInputPlane x 1 x 1 where w and h are spatial image dimensions.
If the input image is a 3D tensor nInputPlane x w x h, the output image size will be nInputPlane x 1 x 1 where w and h are spatial image dimensions.
We note z^c the c-th map of input, and s^c the c-th map of the output.
s^c = log 1 / (h * w) sum_{i,j} exp(beta * z^c_{i,j})
- GlobalMaxPooling: if
beta = +inf - GlobalAveragePooling: if
beta = 0
@inproceedings{pinheiro_weak_seg_cvpr15,
Author = {Pedro O. Pinheiro and Ronan Collobert},
Title = {{From Image-level to Pixel-level Labeling with Convolutional Networks}},
booktitle = {CVPR},
Year = {2015}
}
WeldonPooling is a spatial pooling module used in "WELDON: Weakly Supervised Learning of Deep Convolutional Neural Networks".
module = nn.WeldonPooling(kMax, kMin)Applies 2D WELDON-pooling operation on the whole image. The number of output features is equal to the number of input planes.
The parameters are the following:
kMax: The number of top instances. It is possible to define the number of selected regions (kMax >= 1) or the proportion of selected regions (0 <= kMax < 1). IfkMax < 0,kMaxis set to0. Default iskMax = 1.kMin: The number of low instances. It is possible to define the number of selected regions (kMin >= 1) or the proportion of selected regions (0 <= kMin < 1). IfkMin < 0,kMinis set to0. Default iskMin = 1.
If the input image is a 4D tensor nBatchImage x nInputPlane x w x h, the output image size will be nBatchImage x nInputPlane x 1 x 1 where w and h are spatial image dimensions.
If the input image is a 3D tensor nInputPlane x w x h, the output image size will be nInputPlane x 1 x 1 where w and h are spatial image dimensions.
We note z^c the c-th map of input, and s^c the c-th map of the output.
s^c = max_{h in H_kMax} 1 / kMax sum_{i,j} h_{i,j} z^c_{i,j} + min_{h in H_kMin} 1 / kMin sum_{i,j} h_{i,j} z^c_{i,j}
where H_k is such that h in H_k satisfies h_{i,j} in {0, 1} and sum_{i,j} h_{i,j} = k
- GlobalMaxPooling: if
kMax = 1andkMin = 0 - GlobalAveragePooling: if
kMax = h x wandkMin = 0 - MantraPooling: if
kMax = 1andkMin = 1
@inproceedings{Durand_WELDON_CVPR_2016,
author = {Durand, Thibaut and Thome, Nicolas and Cord, Matthieu},
title = {{WELDON: Weakly Supervised Learning of Deep Convolutional Neural Networks}},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
year = {2016}
}
MIT License