CN110378484A - A kind of empty spatial convolution pyramid pond context learning method based on attention mechanism - Google Patents
A kind of empty spatial convolution pyramid pond context learning method based on attention mechanism Download PDFInfo
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Abstract
The empty spatial convolution pyramid pond context learning method based on attention mechanism that the invention discloses a kind of, be characterized in include: empty spatial convolution pyramid pond model and attention model.The cavity spatial convolution pyramid pond model is made of a plurality of empty convolution path in parallel with different spreading rates, for extracting multiple dimensioned contextual information.The attention model characterizes the relationship between different channel contextual informations by nonlinear function, and then distributes weight to the multi-scale information in each channel.The contextual feature learning ability of empty spatial convolution pyramid pond model can be enhanced in empty spatial convolution pyramid pond context learning method based on attention mechanism of the invention, and can flexibly it be embedded into neural network model, suitable for multiple-tasks such as image, semantic segmentation, target detection, image classifications, it is suitable for promoting and applying.
Description
Technical field
The invention belongs to deep learning field, in particular to a kind of empty spatial convolution pyramid based on attention mechanism
Pond context learning method.
Background technique
Empty spatial convolution pyramid pond model is extracted multiple dimensioned by the empty convolution of multiple and different spreading rates in parallel
Then contextual information carries out linear fusion to each channel using 1 × 1 convolution.However, since multiple dimensioned contextual information is logical
It is normally present in non-linearity manifold, only is not enough to portray the nonlinear dependence between multiple dimensioned contextual information using linear function
System, causes empty spatial convolution pyramid pond model that can not effectively extract multiple dimensioned contextual information.
Summary of the invention
In order to overcome above-mentioned empty spatial convolution pyramid pond model to be difficult to characterize asking for different channel non-linearities relationships
Topic, the present invention provide a kind of empty spatial convolution pyramid pond context learning method based on attention mechanism.
A kind of empty spatial convolution pyramid pond context learning method based on attention mechanism of the invention belongs to
Deep learning field, it is characterised in that include: empty spatial convolution pyramid pond model and attention model, the cavity volume
Product space pyramid pond model is made of a plurality of empty convolution path in parallel with different spreading rates, multiple dimensioned for extracting
Contextual information, the attention model characterize the relationship between different channel contextual informations by nonlinear function, in turn
Weight is distributed to multiple dimensioned contextual information, increases the multiple dimensioned contextual information of empty spatial convolution pyramid pond model
Habit ability.
Cavity spatial convolution pyramid pond model, it is characterised in that the cavity spatial convolution pyramid Chi Huamo
The single access of type can formalize are as follows:
Wherein p is the location index of the corresponding pixel in convolution kernel center, and c is the channel index of input, and d is the expansion
Rate, wC, (i, j)It is the convolution kernel weight of dedicated tunnel and position, xC, p+d (i, j)It is the pixel value of dedicated tunnel and position, G is to adopt
Sample grid, b are bias terms.Input feature vector figure x forms multiple dimensioned by empty spatial convolution pyramid pond model treatment
Characteristic pattern WhereinIt is d for the spreading ratenAccess corresponding to output, more rulers
Degree characteristic pattern is by being spliced to form the input x of attention modelASPP
The input of the attention model is complete by global pool and twice to be connected and obtains Bu Tong leading to after activation primitive
The weight z in road
Z=δ2(W2δ1(W1y))
WhereinIt is attention model input xASPPIn channel c, value corresponding to position (h, w), ycFor channel
The corresponding global pool value of c, y are the tensor obtained after each channel pool value is spliced, δ1And δ2For activation primitive, W1And W2It is complete
The weight of articulamentum.The input x of the attention modelASPPBy being multiplied to obtain multiple dimensioned contextual feature with the weight z
Scheme X
Detailed description of the invention
Fig. 1 is that a kind of empty spatial convolution pyramid pond context learning method based on attention mechanism of the present invention is shown
It is intended to
Specific embodiment
Using drawings and examples, the present invention will be further described below, and attached drawing described herein is used to provide to this
Further understanding for invention, constitutes part of this application, and do not constitute a limitation of the invention.
A kind of empty spatial convolution pyramid pond context learning method schematic diagram based on attention mechanism is shown in attached drawing
1, it is characterised in that include: empty spatial convolution pyramid pond model and attention model, the cavity spatial convolution gold word
Tower basin model is made of a plurality of empty convolution path in parallel with different spreading rates, for extracting multiple dimensioned context letter
Breath, the attention model characterize the relationship between different channel contextual informations by nonlinear function, and then to multiple dimensioned
Contextual information distributes weight, increases the multiple dimensioned contextual information learning ability of empty spatial convolution pyramid pond model.
Cavity spatial convolution pyramid pond model, it is characterised in that the cavity spatial convolution pyramid Chi Huamo
The single access of type can formalize are as follows:
Wherein p is the location index of the corresponding pixel in convolution kernel center, and c is the channel index of input, and d is the expansion
Rate, wC, (i, j)It is the convolution kernel weight of dedicated tunnel and position, xC, p+d (i, j)It is the pixel value of dedicated tunnel and position, G is to adopt
Sample grid, b are bias terms.Input feature vector figure x forms multiple dimensioned by empty spatial convolution pyramid pond model treatment
Characteristic pattern WhereinIt is d for the spreading ratenAccess corresponding to output, more rulers
Degree characteristic pattern is by being spliced to form the input x of attention modelASPP
The input of the attention model is complete by global pool and twice to be connected and obtains Bu Tong leading to after activation primitive
The weight z in road
Z=δ2(W2δ1(W1y))
WhereinIt is attention model input xASPPIn channel c, value corresponding to position (h, w), ycFor channel
The corresponding global pool value of c, y are the tensor obtained after each channel pool value is spliced, δ1And δ2For activation primitive, W1And W2It is complete
The weight of articulamentum.The input x of the attention modelASPPBy being multiplied to obtain multiple dimensioned contextual feature with the weight z
Scheme X
Above-described specific embodiment has carried out further the purpose of the present invention, technical scheme and beneficial effects
It is described in detail, it should be understood that being not intended to limit the present invention the foregoing is merely a specific embodiment of the invention
Protection scope, all within the spirits and principles of the present invention, any modification, equivalent substitution, improvement and etc. done should all include
Within protection scope of the present invention.
Claims (2)
1. a kind of empty spatial convolution pyramid pond context learning method based on attention mechanism, it is characterised in that packet
Contain: empty spatial convolution pyramid pond model and attention model, cavity spatial convolution pyramid pond model is by more
Empty convolution path in parallel of the item with different spreading rates forms, for extracting multiple dimensioned contextual information, the attention mould
Type characterizes the relationship between different channel contextual informations by nonlinear function, and then distributes multiple dimensioned contextual information and weigh
Weight increases the multiple dimensioned contextual information learning ability of empty spatial convolution pyramid pond model.
2. the pyramid pond of cavity spatial convolution described according to claim 1 model, it is characterised in that the cavity convolution is empty
Between the single access of pyramid pond model can formalize are as follows:
Wherein p is the location index of the corresponding pixel in convolution kernel center, and c is the channel index of input, and d is the spreading rate,
wE, (i, j)It is the convolution kernel weight of dedicated tunnel and position, xC, p+d (i, j)It is the pixel value of dedicated tunnel and position, G is sampling network
Lattice, b are bias terms.Input feature vector figure x forms Analysis On Multi-scale Features by the empty spatial convolution pyramid pond model treatment
Figure WhereinIt is d for the spreading ratenAccess corresponding to output, the multiple dimensioned spy
Sign figure is by being spliced to form the input x of attention modelASPP
The input of the attention model obtains different channels after full connection and activation primitive by global pool and twice
Weight z
Z=δ2(W2δ1(W1y))
WhereinIt is attention model input xASPPIn channel c, value corresponding to position (h, w), ycFor c pairs of channel
The global pool value answered, y are the tensor obtained after each channel pool value is spliced, δ1And δ2For activation primitive, W1And W2To connect entirely
The weight of layer.The input x of the attention modelASPPBy being multiplied to obtain multiple dimensioned contextual feature figure X with the weight z
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| CN110910405A (en) * | 2019-11-20 | 2020-03-24 | 湖南师范大学 | Brain tumor segmentation method and system based on multi-scale cavity convolutional neural network |
| CN111046674A (en) * | 2019-12-20 | 2020-04-21 | 科大讯飞股份有限公司 | Semantic understanding method and device, electronic equipment and storage medium |
| CN111159335A (en) * | 2019-12-12 | 2020-05-15 | 中国电子科技集团公司第七研究所 | Short text classification method based on pyramid pooling and LDA topic model |
| CN111179283A (en) * | 2019-12-30 | 2020-05-19 | 深圳市商汤科技有限公司 | Image semantic segmentation method and device and storage medium |
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| CN110910405A (en) * | 2019-11-20 | 2020-03-24 | 湖南师范大学 | Brain tumor segmentation method and system based on multi-scale cavity convolutional neural network |
| CN110910405B (en) * | 2019-11-20 | 2023-04-18 | 湖南师范大学 | Brain tumor segmentation method and system based on multi-scale cavity convolutional neural network |
| CN111159335A (en) * | 2019-12-12 | 2020-05-15 | 中国电子科技集团公司第七研究所 | Short text classification method based on pyramid pooling and LDA topic model |
| CN111046674B (en) * | 2019-12-20 | 2024-05-31 | 科大讯飞股份有限公司 | Semantic understanding method and device, electronic equipment and storage medium |
| CN111046674A (en) * | 2019-12-20 | 2020-04-21 | 科大讯飞股份有限公司 | Semantic understanding method and device, electronic equipment and storage medium |
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