CN114757820A - Semantic-guided content feature transfer style migration method and system - Google Patents

Semantic-guided content feature transfer style migration method and system Download PDF

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CN114757820A
CN114757820A CN202210405069.7A CN202210405069A CN114757820A CN 114757820 A CN114757820 A CN 114757820A CN 202210405069 A CN202210405069 A CN 202210405069A CN 114757820 A CN114757820 A CN 114757820A
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杨大伟
王萌
毛琳
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Dalian Minzu University
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Abstract

The invention discloses a semantic-guided content feature transfer style migration method and a semantic-guided content feature transfer style migration system, and belongs to the field of deep learning style migration. In order to realize the style migration with complete and consistent content features, the invention provides a content calibration module which comprises a feature optimization unit and an attribute reasoning unit. The feature optimization unit keeps the multi-channel content features complete by utilizing the network deep extraction capability, the attribute reasoning unit ignores the position space, and reclassifies the content semantics by means of an attention grouping interaction mode to provide help for searching proper content expression. And the content attribute extracted from the original content feature is endowed to the deep content feature again, the content feature mapping deviation is calibrated, the content feature noise is reduced, and the content consistency is achieved. The invention is suitable for the fields of automatic driving, security monitoring and the like.

Description

Semantic-guided content feature transfer style migration method and system
Technical Field
The invention relates to the technical field of deep learning style migration, in particular to a semantic-guided content feature transfer style migration method and system.
Background
With the rapid development of the application fields of automatic driving and industrial and service robots, a style migration technology essential to the automatic driving and path planning perception system becomes one of the current research hotspots. In the aspect of hardware, most of automatic driving systems rely on equipment such as radars and infrared cameras to improve the sensing capability of the surrounding environment of the system during driving, but the cost is high, and small targets and high-speed moving targets are not accurately positioned and predicted; in the aspect of software, the performance of the existing style migration method is improved mostly by deepening and widening a network or improving a loss function, but content mapping deviation is easily generated in a training process, complete and consistent feature transfer of contents is difficult to realize, and driving safety of an automatic driving system is influenced. Existing style migration methods can be divided into two categories, namely convolutional neural network-based and generation-based countermeasure networks:
the style migration method based on the convolutional neural network generally converts a content image into a given style image by optimizing or training an image migration neural network by means of a classification neural network. Specifically, the invention discloses an image style migration method integrating depth learning and depth perception, and the invention patent with the publication number of CN107705242B discloses that an integrated depth perception network adds depth loss to an object loss function to estimate the depth of field of an original image and a generated style image. In the style migration process, the generated image not only fuses corresponding styles and contents, but also keeps the far and near structure information of the original image. The invention patent application with the publication number of CN13837926A constructs a feature space to store feature information of different filters, thereby better obtaining multi-scale and stable features, without training real data, and flexibly performing style transformation. The invention discloses a style migration method, a device and related components based on feature fusion, and in the invention patent application with the publication number of CN113808011A, features of content and style images are extracted through a pre-trained content and style encoder, and then the trained content and style decoder is used for fusing and outputting the content and style features to obtain a target style migration image. The style migration method based on the convolutional neural network focuses on extracting content and style characteristics in an image by deepening or widening a network through the VGG and other functional layers, and the generated style migration effect is crossed in the aspects of detail texture and color filling, so that the method cannot be well applied to style migration of real scenes such as automatic driving and mobile robots.
The style migration method based on the generation countermeasure network accelerates the progress of the style migration field, generally based on an encoding-decoding structure, utilizes an encoder to synchronously extract content characteristics and style characteristics, directly inputs the two characteristics into a decoder for decoding, and simultaneously designs a related loss function from the aspects of color, content, smoothness and the like to monitor the network to obtain stylized results. The invention discloses a cross-domain variation confrontation self-coding method, which is characterized in that an encoder is utilized to decouple content coding and style coding of a cross-domain input image, confrontation operation and variation operation are utilized to respectively fit the content and style coding of the image, and content and style coding of different domains are crossed to realize one-to-many transformation of the cross-domain image. The invention discloses an image multi-format conversion method based on latent variable feature generation, and the invention patent application with the publication number of CN11099225A designs a style code generator to fit the style code of an image on the basis of a multi-mode unsupervised image conversion network, introduces jump connection between content coding and style coding, introduces an attention mechanism in the style coding, and improves the quality and diversity of image multi-format conversion. The invention discloses a multi-path parallel image content feature optimization style migration method, and the invention patent application with the publication number of CN113284042A separates the image content features of a single feature channel and a plurality of feature channels by using a multi-path parallel mode, and can improve the separation and extraction capacity of small targets and fuzzy targets and the migration capacity of image detail texture information. However, most of the style migration methods perform reasoning according to the network performance in a closed environment and in combination with context information, and the training process is difficult to avoid the influence of various confusion factors such as different target attributes and the like, so that the actual output and the theoretical output of the network have deviation. Therefore, how to effectively utilize the depth features extracted from the images, ensure the consistency of the image contents before and after the style migration, and better apply the depth features to the traffic scene becomes a problem to be solved urgently.
Disclosure of Invention
The invention aims to provide a semantic-guided content feature transfer style migration method and system, which divide noisy attributes generated by content features into a plurality of groups in a channel attention mode, respectively perform random information exchange between different groups and the same group, weaken noise, perform channel-by-channel fusion correction on the rest content features, endow correct content attribute labels for the content features in the transfer process, guide the generation of the content features, reduce the mapping deviation in the feature transfer process and effectively realize style migration with consistent image content.
In order to achieve the purpose, the technical scheme of the invention is as follows: a semantically guided content feature delivery style migration method comprises the following steps:
preparing a data set of a training style migration network;
obtaining a source domain input image with a characteristic channel of c
Figure BDA0003601907260000021
And target domain input image
Figure BDA0003601907260000022
Respectively carrying out double down-sampling operation on the data, wherein the double down-sampling operation comprises convolution operation and nonlinear activation function processing;
downsampling results for target domain input images
Figure BDA0003601907260000023
Obtaining style feature vectors by using global average pooling and full-join function processing
Figure BDA0003601907260000024
Downsampling results for source domain input images
Figure BDA0003601907260000025
Obtaining four-dimensional characteristic vector by adopting multi-layer residual error unit processing
Figure BDA0003601907260000026
The four-dimensional feature vector
Figure BDA0003601907260000027
Sequentially processing the four-dimensional feature vectors by global maximum pooling and full-link function processing, and processing the four-dimensional feature vectors by a deep convolutional neural network, information exchange and a point convolutional neural network to obtain four-dimensional feature vectors
Figure BDA0003601907260000028
Deepening four-dimensional feature vector by simultaneously using multilayer residual error unit
Figure BDA0003601907260000029
Obtaining four-dimensional feature vectors
Figure BDA00036019072600000210
The four-dimensional feature vector is processed
Figure BDA00036019072600000211
And four-dimensional feature vectors
Figure BDA00036019072600000212
Multiplying to generate four-dimensional content feature vector
Figure BDA00036019072600000213
Reallocating target attributes in the content features and correcting feature transfer deviation;
the style feature vector is combined
Figure BDA00036019072600000214
And four-dimensional content feature vectors
Figure BDA00036019072600000215
Adding and fusing to obtain four-dimensional feature vector
Figure BDA00036019072600000216
Then, the style migration result Y is output by up-samplingc×2h×2w。
Further, inputting images to the source domain
Figure BDA00036019072600000217
And target domain input image
Figure BDA00036019072600000218
Performing double down-sampling operation, specifically:
using convolution kernels Mc×3×3Extracting the source domain input image
Figure BDA0003601907260000031
The content characteristics of
Figure BDA0003601907260000032
And the target domain input image
Figure BDA0003601907260000033
The style characteristic of
Figure BDA0003601907260000034
The formula is as follows:
Figure BDA0003601907260000035
Figure BDA0003601907260000036
wherein
Figure BDA0003601907260000037
For the convolution process, each matrix represents a 3 × 3 feature block;
feature vector to be output
Figure BDA0003601907260000038
And
Figure BDA0003601907260000039
using nonlinear activation function processing, when the characteristic value of the activation processing is less than or equal to 0, the output value of the activation function is 0, as shown in formula (3); conversely, if the activation function output value is the same as the input value, as shown in equation (4):
Figure BDA00036019072600000310
Figure BDA00036019072600000311
wherein the function A (-) is an activation function.
Further, down-sampling result of the target domain input image
Figure BDA00036019072600000312
And (3) processing by using a global average pooling function and a full-connection function, specifically:
averaging the features of each unit by using global average pooling to obtain a feature vector of each unit
Figure BDA00036019072600000313
The formula is as follows:
Figure BDA00036019072600000314
wherein, Paverage(. is a global average pooling function, Mc×2×2Performing pixel-by-pixel operation on the convolution kernel characteristic of the filter k-2, selecting an average value and outputting the average value;
for feature vector
Figure BDA00036019072600000315
Processing the feature channels one by using a full-connection function, and outputting feature vectors
Figure BDA00036019072600000316
The formula is as follows:
Figure BDA00036019072600000317
wherein, Cfully(. for) a full connection function, using Mc×1×1I.e. the convolution kernel of filter k 1 operates;
for feature vector
Figure BDA00036019072600000318
Performing style cosine normalization processing to obtain four-dimensional style feature vector
Figure BDA00036019072600000319
The formula is as follows:
Figure BDA00036019072600000320
wherein cosIN(. cndot.) is a style cosine normalization process function, μ (x) and μ (y) are means in the length and width dimensions of the feature vector, respectively, and σ (x) and σ (y) are standard deviations in the length and width dimensions of the four-dimensional feature vector, respectively.
Further, down-sampling result of the source domain input image
Figure BDA0003601907260000041
Adopting multi-layer residual error unit processing, and the formula is as follows:
Figure BDA0003601907260000042
Figure BDA0003601907260000043
where F (-) is a single-layer residual unit process function, ω3Is a weight matrix.
Further, the four-dimensional feature vector
Figure BDA0003601907260000044
Sequentially carrying out global maximum pooling, full-connection function processing, deep convolutional neural network, information exchange and point convolutional neural network processing to obtain four-dimensional feature vectors
Figure BDA0003601907260000045
The method specifically comprises the following steps:
processing four-dimensional feature vectors using global maximum pooling
Figure BDA0003601907260000046
And obtaining the feature vector
Figure BDA0003601907260000047
The formula is as follows:
Figure BDA0003601907260000048
wherein, Pmax(. is a global maximum pooling function, Mc×2×2Carrying out pixel-by-pixel operation on the convolution kernel characteristic of the filter k-2, selecting the maximum value and outputting the maximum value;
processing feature vectors using full join functions
Figure BDA0003601907260000049
And obtaining the feature vector
Figure BDA00036019072600000410
The formula is as follows:
Figure BDA00036019072600000411
wherein, Cfully(. for) a full connection function, using Mc×1×1I.e. the convolution kernel of filter k 1 operates;
feature vector using deep convolutional neural network
Figure BDA00036019072600000412
Uniformly dividing the characteristic channels into p branches (p is less than or equal to c) to obtain characteristic components of each characteristic channel
Figure BDA00036019072600000413
The formula is as follows:
Figure BDA00036019072600000414
wherein, Fdeep(. h) is a deep convolutional neural network process function;
randomly exchanging features by q groups on each branch, disturbing the inherent sequence of information between different channels, and reclassifying and combining the feature information to obtain feature components
Figure BDA00036019072600000415
The formula is as follows:
Figure BDA00036019072600000416
wherein, Shuffle (-) is an information exchange function;
using point convolution neural networks to characterize components
Figure BDA0003601907260000051
Merging to obtain four-dimensional characteristic vector
Figure BDA0003601907260000052
The point convolution neural network randomly deletes part of neurons in the merging process, and the formula is as follows:
Figure BDA0003601907260000053
wherein D isranIs a random deletion function, and m is the proportion of randomly deleted neurons;
Figure BDA0003601907260000054
wherein, Fpoi(. to) a point convolution neural network process function, using Mc×1×1A formal point convolution performs a point convolution operation on the feature vector.
Further, the feature vectors are further processed using a multi-layer residual unit
Figure BDA0003601907260000055
Obtaining four-dimensional feature vectors
Figure BDA0003601907260000056
The method comprises the following specific steps:
Figure BDA0003601907260000057
wherein the content of the first and second substances,
Figure BDA0003601907260000058
is a weight matrix.
Further, the feature vector is used
Figure BDA0003601907260000059
And four-dimensional feature vectors
Figure BDA00036019072600000510
Multiplying to generate four-dimensional content feature vector
Figure BDA00036019072600000511
The method specifically comprises the following steps:
Figure BDA00036019072600000512
wherein the content of the first and second substances,
Figure BDA00036019072600000513
and
Figure BDA00036019072600000514
for the weight matrix, x represents the feature matrix multiplication.
The invention also provides a semantic-guided content feature transfer style migration system, which comprises an encoding module, a content calibration module and a decoding module;
the encoding module comprises a content encoding module and a style encoding module; the content encoding module inputs source domain input images
Figure BDA00036019072600000515
As input, double down sampling operation is performed on the four-dimensional feature vector to output the four-dimensional feature vector
Figure BDA00036019072600000516
The style encoding module inputs the target domain into the image
Figure BDA0003601907260000061
As input, sequentially using double down sampling, global average pooling, full-connected function and style cosine normalization processing to output four-dimensional feature vector
Figure BDA0003601907260000062
The content calibration module comprises a feature optimization unit and an attribute reasoning unit; the characteristic optimization unit is used for down-sampling the source domain input image
Figure BDA0003601907260000063
Obtaining four-dimensional characteristic vector by adopting multi-layer residual error unit processing
Figure BDA0003601907260000064
The above-mentionedAttribute inference unit pairs four-dimensional feature vectors
Figure BDA0003601907260000065
Sequentially processing the four-dimensional feature vectors by global maximum pooling and full-link function processing, and processing the four-dimensional feature vectors by a deep convolutional neural network, information exchange and a point convolutional neural network to obtain four-dimensional feature vectors
Figure BDA0003601907260000066
Deepening processing of feature vectors using multi-layer residual unit simultaneously
Figure BDA0003601907260000067
Obtaining four-dimensional feature vectors
Figure BDA0003601907260000068
Combining four-dimensional feature vectors
Figure BDA0003601907260000069
And four-dimensional feature vector
Figure BDA00036019072600000610
In a fixed ratio omega1And omega2Multiplying and outputting four-dimensional feature vector
Figure BDA00036019072600000611
The decoding module is used for decoding the four-dimensional feature vector
Figure BDA00036019072600000612
And four-dimensional feature vector
Figure BDA00036019072600000613
Adding and fusing to obtain four-dimensional feature vector
Figure BDA00036019072600000614
Then, the style migration result Y is output by up-samplingc×2h×2w。
Further, the content calibration module is expressed as:
Figure BDA00036019072600000615
wherein, Fopt(x) Learning a process function for the feature optimization unit, Fre(x) A process function is learned for the attribute reasoning unit.
Due to the adoption of the technical scheme, the invention can obtain the following beneficial effects: the method can be applied to real scenes such as automatic driving, industrial and service robots, can realize style transformation of any weather and environment scene, and provides help for accurately identifying small targets and fuzzy targets. The following points are listed and introduced for the beneficial effects of the invention:
(1) adapted for small target feature cases
The attribute reasoning unit can separate the potential example target attributes in the image content characteristics, fully excavate depth characteristic information, and accurately and clearly identify and extract the content characteristic information containing the small target image under the unsupervised condition.
(2) Is suitable for the characteristic situation of the high-speed moving object
The invention uses the characteristic optimization unit and the attribute reasoning unit to respectively extract the content characteristics and the target attributes in the input image, corrects the characteristic transfer deviation in the characteristic optimization unit according to the extracted target attributes, effectively improves the fuzzy phenomenon generated by the high-speed movement of the target and realizes the extraction work of the high-speed moving target.
(3) Monitoring system suitable for public security
The invention can meet the requirements of multi-scale feature effective extraction and style transformation in all-weather any complex scenes (such as underground parking lots, fire fighting passageways and traffic scenes) shot by a security monitoring camera and a sky-eye system, provides favorable conditions for next detection and identification work, improves the working efficiency of a public system, and provides guarantee for improving the production and living efficiency and maintaining public safety.
(4) Adapted for autonomous driving technique
The invention is a computer vision environment perception technology, is suitable for the field of automatic driving, can extract target characteristics and positions of pedestrians, vehicles, buildings, traffic signs and the like around a driving environment, provides comprehensive characteristic information for a style migration model, and provides powerful guarantee for driving safety.
(5) Is suitable for the situation of unclear vision
The method is suitable for different complex scene style migration situations, the characteristics of the visual unclear target obtained by the camera lens with different exposure degrees and definition degrees under the infrared and visible light conditions are recovered, and the style migration is carried out after the definition of the image is improved.
Drawings
FIG. 1 is a flow diagram of a semantically guided content feature delivery style migration method;
FIG. 2 is a schematic diagram of a content calibration module;
FIG. 3 is a schematic diagram of a migration situation of a security monitoring style in embodiment 1;
FIG. 4 is a schematic view of the autonomous driving style transition in embodiment 2;
fig. 5 is a schematic diagram of the visual blur scene style transition in embodiment 3.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the application, i.e., the embodiments described are only a subset of, and not all embodiments of the application. The components of the embodiments of the present application, generally described and illustrated in the figures herein, can be arranged and designed in a wide variety of different configurations.
The invention provides a semantic-guided content feature transfer style migration method and system, which firstly enhance the extraction capability of depth features through deepening a network, ensure the integrity of image content features in the style migration process and reduce feature redundancy; secondly, compressing the content characteristics into one-dimensional semantic information expression, carrying out information exchange in batches channel by channel, enhancing the association of different channel characteristics, distributing a correct content label for each content characteristic, improving the phenomenon that the target attribute is not matched with the content characteristic expression in the characteristic transmission process, and ensuring the transmission consistency of the content characteristics. As shown in fig. 1, the specific migration method includes the following steps:
step 1: preparing a data set of the training style migration network, wherein the size of the data set can be 2h multiplied by 2 w;
step 2: obtaining a source domain input image with a characteristic channel of c
Figure BDA0003601907260000071
And target domain input image
Figure BDA0003601907260000072
Respectively carrying out double down-sampling operation on the data, wherein the double down-sampling operation comprises convolution operation and nonlinear activation function processing, and specifically comprises the following steps:
(1) convolution kernel M using step size s-2 and filter k-3c×3×3Extracting the source domain input image
Figure BDA0003601907260000073
The content characteristics of
Figure BDA0003601907260000074
And the target domain input image
Figure BDA0003601907260000075
The style characteristic of
Figure BDA0003601907260000076
The formula is as follows:
Figure BDA0003601907260000077
Figure BDA0003601907260000078
wherein
Figure BDA0003601907260000079
For the convolution process, each matrix represents a 3 × 3 feature block;
(2) feature vector to be output
Figure BDA00036019072600000710
And
Figure BDA00036019072600000711
using nonlinear activation function processing, when the characteristic value of the activation processing is less than or equal to 0, the output value of the activation function is 0, as shown in formula (3); conversely, when the output value of the activation function is the same as the input value, as shown in equation (4):
Figure BDA0003601907260000081
Figure BDA0003601907260000082
the function A (-) is an activation function, the effectiveness of the feature vector can be improved and the feature redundancy can be reduced by carrying out nonlinear processing on the feature vector by adopting the activation function, and help is provided for realizing the feature consistency style migration of the image content.
And 3, step 3: in order to reduce the influence of the feature position on the style classification, the feature vector extracted in the step 2 is used
Figure BDA0003601907260000083
Output feature vectors using global average pooling and full-join function processing
Figure BDA0003601907260000084
The method comprises the following specific steps:
(1) averaging the features of each unit by using global average pooling to obtain a feature vector of each unit
Figure BDA0003601907260000085
The formula is as follows:
Figure BDA0003601907260000086
wherein, Paverage(. is a global average pooling function, Mc×2×2Performing pixel-by-pixel operation on the convolution kernel features with k being 2, selecting an average value and outputting the average value;
(2) for feature vector
Figure BDA0003601907260000087
The full-connection function is used for processing the characteristic channels one by one, the influence of pixels and characteristic positions on characteristic classification is reduced, and characteristic vectors are output
Figure BDA0003601907260000088
The formula is as follows:
Figure BDA0003601907260000089
wherein, Cfully(. for) a full connection function, using Mc×1×1I.e., the convolution kernel with k equal to 1, operates.
(3) In order to change the style feature data distribution, accurate style feature transmission is realized. The invention is to the feature vector
Figure BDA00036019072600000810
Performing style cosine normalization processing, suppressing feature information irrelevant to style, and outputting four-dimensional feature vector
Figure BDA00036019072600000811
In preparation for fusing with content features, the formula is:
Figure BDA00036019072600000812
wherein cosIN(. is a stylized cosine normalized process function, having a μ (x) and μ (y) scoreRespectively, mean values in the length and width dimensions of the feature vector, and σ (x) and σ (y) are standard deviations in the length and width dimensions of the four-dimensional feature vector, respectively.
And 4, step 4: the characteristic optimization unit inputs the down-sampling result of the source domain input image
Figure BDA00036019072600000813
And a multi-layer (preferably 8-layer) residual error unit is adopted to process the feature vectors output by downsampling, so that feature redundancy is reduced, and the image content is ensured to be complete in the style migration process. The formula is as follows:
Figure BDA0003601907260000091
Figure BDA0003601907260000092
where F (-) is a single-layer residual unit process function, ω3Is a weight matrix;
and 5, step 5: the attribute reasoning unit inputs four-dimensional feature vectors
Figure BDA0003601907260000093
The four-dimensional feature vector is output after global maximum pooling, full-connection function processing, deep convolution neural network, information exchange and point convolution neural network processing in sequence
Figure BDA0003601907260000094
And (4) intervening in a backstepping mechanism in the network training process, and endowing the content characteristics with correct content attribute labels again. Meanwhile, four-dimensional feature vectors are deeply processed by using a multi-layer residual error unit
Figure BDA0003601907260000095
Outputting four-dimensional feature vectors
Figure BDA0003601907260000096
Consistent delivery of spatial structure information in implementation features, in particularComprises the following steps:
(1) to extract hidden target attributes in content features, the present invention uses global max-pooling to process four-dimensional feature vectors
Figure BDA0003601907260000097
And output
Figure BDA0003601907260000098
Eliminating the classification influence of the target attributes caused by the target position, and the formula is
Figure BDA0003601907260000099
Wherein, Pmax(. is a global maximum pooling function, Mc×2×2Carrying out pixel-by-pixel operation on the convolution kernel characteristic with k being 2, selecting the maximum value and outputting the maximum value;
(2) to enhance the learning ability of the network to hidden content attributes, a full-connection function process is used
Figure BDA00036019072600000910
Enhancing correlation between channels and outputting characteristic vector
Figure BDA00036019072600000911
The formula is as follows:
Figure BDA00036019072600000912
wherein, Cfully(. for) a full connection function, using Mc×1×1I.e., the convolution kernel with k equal to 1;
(3) feature vector transformation using deep convolutional neural network
Figure BDA00036019072600000913
Uniformly dividing the characteristic channel into p branches (p is less than or equal to c) to obtain the characteristic component of each characteristic channel
Figure BDA00036019072600000914
The formula is as follows:
Figure BDA00036019072600000915
wherein, Fdeep(. h) is a deep convolutional neural network process function;
(4) randomly exchanging features by q groups on each branch, disturbing the inherent order of information between different channels, reclassifying and combining the feature information, and outputting feature components
Figure BDA00036019072600000916
The formula is as follows:
Figure BDA00036019072600000917
wherein, Shuffle (-) is an information exchange function, channel characteristics on each branch are divided into q groups, and the sequence is randomly disturbed between each group and different groups, so as to seek a new content and attribute matching relationship.
(5) Merging the interacted feature vectors by using a point convolution neural network, and outputting a content inference unit result
Figure BDA0003601907260000101
The recombination and fusion of the characteristics among different characteristic channels provides more possibility for the accurate transmission of the content characteristics. The point convolution neural network can randomly delete part of neurons in the processing process, and the formula is as follows:
Figure BDA0003601907260000102
wherein D isranThe operation can prevent the network from generating an overfitting phenomenon because a random deleting function is adopted and m is the proportion of randomly deleting neurons;
Figure BDA0003601907260000103
wherein, Fpoi(. to) a point convolution neural network process function, using Mc×1×1A formal point convolution performs a point convolution operation on the feature vector.
(6) Processing four-dimensional feature vectors using multi-layer residual units
Figure BDA0003601907260000104
And outputs the four-dimensional feature vector
Figure BDA0003601907260000105
The consistency of the spatial structure and the semantics in the transmission process of the feature vector is ensured, and a foundation is laid for the effective interaction of the feature components of different feature channels.
Figure BDA0003601907260000106
Wherein the content of the first and second substances,
Figure BDA0003601907260000107
is a weight matrix.
And 6, step 6: outputting the four-dimensional feature vector from the step 5
Figure BDA0003601907260000108
And four-dimensional feature vector
Figure BDA0003601907260000109
Multiplying to generate four-dimensional feature vector
Figure BDA00036019072600001010
Realizing the redistribution of target attributes in the content characteristics and correcting the characteristic transmission deviation, wherein the formula is as follows:
Figure BDA00036019072600001011
wherein the content of the first and second substances,
Figure BDA00036019072600001012
and
Figure BDA00036019072600001013
for the weight matrix, x represents the feature matrix multiplication.
And 7, step 7: the style characteristics output by the step 3
Figure BDA00036019072600001014
And the content characteristics output in step 6
Figure BDA00036019072600001015
Adding and fusing to obtain four-dimensional feature vector
Figure BDA00036019072600001016
Upsampling output style migration result Y in decoderc×2h×2w。
The present embodiment further provides a system for implementing the method, which includes: the device comprises an encoding module, a content calibration module and a decoding module; each of these sections is described in detail below:
the encoding module comprises a content encoding module and a style encoding module; the content encoding module inputs source domain input images
Figure BDA0003601907260000111
As input, double down sampling operation is performed on the four-dimensional feature vector to output the four-dimensional feature vector
Figure BDA0003601907260000112
The style encoding module inputs the target domain into the image
Figure BDA0003601907260000113
As input, the four-dimensional feature vector is output by sequentially using global average pooling, full-connected function and style cosine normalization processing
Figure BDA0003601907260000114
The interference of the spatial structure on the color semantic information is reduced.
The content calibration module comprises a feature optimization unit and an attribute reasoning unit; as shown in fig. 2, the four-dimensional feature vector of the multi-feature channel is input into the feature optimization unit, and more content structure and detail texture information are learned while the image content features of the multi-feature channel are completely transferred; the attribute reasoning unit extracts hidden attributes from the four-dimensional feature vectors output by the residual error unit, and gives correct feature expression to each content feature again through multi-channel and batch training, so that the accurate correspondence of one-dimensional semantics and two-dimensional pixels is realized. The outputs of different units are multiplied in a channel attention mode, the content feature transfer deviation is corrected, the accurate classification of a content structure is realized, and the image content feature transfer consistency is implemented; the feature optimization unit and the attribute reasoning unit in the content calibration module are explained in detail as follows: the input of the attribute reasoning unit is a four-dimensional feature vector which comprises n feature channels and has the size of h multiplied by w after being processed by a multilayer residual error unit in the feature optimization unit
Figure BDA0003601907260000115
Wherein the learning process function of the feature optimization unit is Fopt(x) (ii) a The attribute reasoning unit learning process function is Fre(x) (ii) a The four-dimensional feature vector which comprises n feature channels and has the size of h multiplied by w is input to the next stage after the two unit output features are fused
Figure BDA0003601907260000116
The content calibration module is expressed as:
Figure BDA0003601907260000117
the feature optimization unit extracts depth features having n feature channels using a multi-layer residual unit
Figure BDA0003601907260000118
And feature redundancy is reduced, and texture details and contour feature information of the input image in the source domain are kept. And takes it as input to the attribute reasoning unit and the next stage.
Attribute inference unit using global average pooling pairs
Figure BDA0003601907260000119
Performing dimension reduction and regularization processing to output four-dimensional feature vectors
Figure BDA00036019072600001110
On the basis, full-connection layer processing is used, the influence of spatial information such as target positions on content attribute classification is weakened, so that the network is concentrated on the association among different channel features, semantic feature expression is enhanced, and the extraction capability of depth features is enhanced. In order to further enhance the relevance among different characteristic channels, the hidden attribute in the source domain input image is fully extracted, and the deep convolution with the convolution kernel of 3 multiplied by 3 is utilized to carry out the method
Figure BDA00036019072600001111
The method is divided into p branches, content characteristic information on each characteristic channel is respectively extracted, different branches learn and supervise mutually, and cross-characteristic channel reference is achieved. Meanwhile, the characteristic channels are divided into q groups in each branch, the channel sequence is disturbed in each group and different groups, the randomness of hidden content attributes is increased, the network generalization capability is improved, the classification attributes of the target are redistributed for each content characteristic, and the characteristic transfer deviation is reduced. In order to obtain accurate content attributes, the characteristics of each branch are filtered to obtain
Figure BDA00036019072600001112
Completing the integration work of p branch characteristic information by using 1 x 1 point convolution and outputting four-dimensional characteristic vectors
Figure BDA00036019072600001113
Each content feature is endowed with enhanced feature expression to guide the accurate generation of the content features. While processing the feature vectors in a single eigen-channel, use multi-layer residual units in parallel branches to face at multiple eigen-channel levels
Figure BDA00036019072600001114
Processing and outputting four-dimensional feature vector
Figure BDA00036019072600001115
Ensuring the transmission integrity of the feature vector; will be provided with
Figure BDA00036019072600001116
And
Figure BDA00036019072600001117
in a fixed ratio omega1And ω2Multiplying and outputting four-dimensional feature vector
Figure BDA00036019072600001118
The hidden attributes and the inherent content characteristics are subjected to countermeasure screening, the problem of wrong distribution of the content attributes is solved, the transmission deviation of the content characteristics is reduced, and help is provided for realizing consistent and accurate style migration of the content.
The decoding module performs source domain and target domain feature vector operations: will be provided with
Figure BDA0003601907260000121
And
Figure BDA0003601907260000122
adding and fusing to obtain four-dimensional feature vector
Figure BDA0003601907260000123
Then, the style migration result Y is output by up-samplingc×2h×2w。
The feature parameter constraint condition in this embodiment may be:
(1) the RGB three-channel image with a size of 256 × 256 is down-sampled, and the input image size is reduced to 128 × 128, the feature channel n ∈ {4,8,16,64,256,512}, and any one of {1,128, 4}, {1,128, 8}, {1,128, 16}, {1,128, 64}, {1,128,128,256}, and {1,128,128,512} can be output as a four-dimensional feature vector containing features of image contents.
(2) The content calibration module selects four-dimensional feature vectors of different feature channels as input according to different input image contents: when the input image contains a small target or a fuzzy target, selecting a four-dimensional feature vector with a feature channel of n-256 as the input of a content calibration module; when no small target or fuzzy target exists in the input image, a four-dimensional feature vector with a feature channel of n being 8 is selected as the input of the content calibration module.
(3) And the feature optimization unit transmits four-dimensional feature vectors with feature channels n epsilon {4,8,16,64,256 and 512 }.
(4) And the attribute reasoning unit transmits a four-dimensional feature vector with a feature channel n being 1.
Structural unit constraint conditions:
(1) and the feature optimization unit extracts the depth content features by using a 4-layer residual error unit.
(2) The attribute reasoning unit comprises p branches, and p is equal to {0,1,2,3,4 }. When p is 0, the content calibration module only contains the feature optimization unit.
(3) Each branch in the attribute reasoning unit contains q groups, wherein q is { q |10 ≦ q ≦ 512, q belongs to Z+}。
(4) The attribute reasoning unit selects different grouping numbers according to different input image content complexity: when the input image contains a small target or a fuzzy target, selecting q ═ { q |128 ≦ q ≦ 512, and q ∈ Z+The number of packets; when no small target or fuzzy target exists in the input image, selecting q ═ { q |10 ≦ q ≦ 128, q ∈ Z+The number of packets.
Example 1: security monitoring style migration situation
The embodiment is used for monitoring unmanned prevention and places with multiple accidents, such as schools, crossroads and the like. The method is used for outdoor safety monitoring, and the identification capability of the target under complex illumination can be effectively improved. The security monitoring image style migration situation is shown in fig. 3.
Example 2: autonomous driving style migration scenario
The present example is directed to autonomous driving system style migration. The invention is applied to the vehicle-mounted camera, senses the surrounding environment of the vehicle, provides an auxiliary means for a driver, reduces the traffic accident rate, improves the safe driving capability of the vehicle, and has the autonomous driving style migration situation as shown in figure 4.
Example 3: visual-blurred scene style migration scenarios
The method can improve the image quality of the style migration of the visual fuzzy scene caused by the conditions of uneven illumination or natural weather, and the like, and prepares for next target detection or image segmentation, wherein the style migration condition of the visual fuzzy scene is shown in fig. 5.
It will be appreciated by those of ordinary skill in the art that the embodiments described herein are intended to assist the reader in understanding the principles of the invention and are to be construed as being without limitation to such specifically recited embodiments and examples. All such possible equivalents and modifications are deemed to fall within the scope of the invention as defined in the claims.

Claims (9)

1. A semantic-guided content feature transfer style migration method is characterized by comprising the following steps:
preparing a data set of a training style migration network;
acquiring source domain input image with characteristic channel c
Figure FDA0003601907250000011
And target domain input image
Figure FDA0003601907250000012
Respectively carrying out double down-sampling operation on the double down-sampling operation, wherein the double down-sampling operation comprises convolution operation and nonlinear activation function processing;
downsampling results for target domain input images
Figure FDA0003601907250000013
Obtaining style feature vectors by using global average pooling and full-connection function processing
Figure FDA0003601907250000014
Downsampling results for source domain input images
Figure FDA0003601907250000015
Obtaining four-dimensional characteristic vector by adopting multi-layer residual error unit processing
Figure FDA0003601907250000016
The four-dimensional feature vector
Figure FDA0003601907250000017
Sequentially carrying out global maximum pooling, full-connection function processing, deep convolutional neural network, information exchange and point convolutional neural network processing to obtain four-dimensional feature vectors
Figure FDA0003601907250000018
Deepening four-dimensional feature vector by simultaneously using multilayer residual error unit
Figure FDA0003601907250000019
Obtaining four-dimensional feature vectors
Figure FDA00036019072500000110
The four-dimensional feature vector is processed
Figure FDA00036019072500000111
And four-dimensional feature vectors
Figure FDA00036019072500000112
Multiplying to generate four-dimensional content feature vector Y1 c ×h×wReallocating the target attribute in the content characteristics and correcting the characteristic transmission deviation;
the style feature vector is converted into a plurality of style feature vectors
Figure FDA00036019072500000113
And four-dimensional content featureEigenvector Y1 c×h×wAdding and fusing to obtain four-dimensional feature vector
Figure FDA00036019072500000114
Then, the style migration result Y is output by up-samplingc×2h×2w。
2. The method of claim 1, wherein the source domain input image is input into a semantic-guided content feature transfer style migration method
Figure FDA00036019072500000115
And target domain input image
Figure FDA00036019072500000116
Performing double down-sampling operation, specifically:
using convolution kernel Mc×3×3Extracting the source domain input image
Figure FDA00036019072500000117
The content characteristics of
Figure FDA00036019072500000118
And the target domain input image
Figure FDA00036019072500000119
The style characteristic of
Figure FDA00036019072500000120
The formula is as follows:
Figure FDA00036019072500000121
Figure FDA00036019072500000122
wherein
Figure FDA00036019072500000123
For the convolution process, each matrix represents a 3 × 3 feature block;
feature vector to be output
Figure FDA00036019072500000124
And
Figure FDA00036019072500000125
using nonlinear activation function processing, and when the characteristic value of the activation processing is less than or equal to 0, setting the output value of the activation function to be 0, as shown in formula (3); conversely, when the output value of the activation function is the same as the input value, as shown in equation (4):
Figure FDA00036019072500000126
Figure FDA00036019072500000127
wherein the function A (-) is an activation function.
3. The method of claim 1, wherein the downsampling of the target domain input image results in the semantic-guided content feature delivery style migration method
Figure FDA0003601907250000021
Processing by using a global average pooling and full join function, specifically:
averaging the features of each unit by using global average pooling to obtain a feature vector of each unit
Figure FDA0003601907250000022
The formula is as follows:
Figure FDA0003601907250000023
wherein, Paverage(. is a global average pooling function, Mc×2×2Performing pixel-by-pixel operation on the convolution kernel characteristic of the filter k-2, selecting an average value and outputting the average value;
for feature vector
Figure FDA0003601907250000024
Processing the characteristic channels one by using a full-connection function, and outputting a characteristic vector
Figure FDA0003601907250000025
The formula is as follows:
Figure FDA0003601907250000026
wherein, Cfully(. cndot.) is a full connection function, using Mc×1×1I.e. the convolution kernel of filter k ═ 1 operates;
for feature vector
Figure FDA0003601907250000027
Performing style cosine normalization processing to obtain four-dimensional style feature vector
Figure FDA0003601907250000028
The formula is as follows:
Figure FDA0003601907250000029
wherein cosIN(. cndot.) is a style cosine normalization process function, μ (x) and μ (y) are means in the length and width dimensions of the feature vector, respectively, and σ (x) and σ (y) are standard deviations in the length and width dimensions of the four-dimensional feature vector, respectively.
4. The method of claim 1, wherein the downsampling of the source domain input image results in a semantic-guided content feature delivery style migration method
Figure FDA00036019072500000210
Adopting multi-layer residual error unit processing, and the formula is as follows:
Figure FDA00036019072500000211
Figure FDA00036019072500000212
where F (-) is a single-layer residual unit process function, ω3Is a weight matrix.
5. The method of claim 1, wherein the four-dimensional feature vector is used for transferring the content feature transfer style
Figure FDA00036019072500000213
Sequentially carrying out global maximum pooling, full-connection function processing, deep convolutional neural network, information exchange and point convolutional neural network processing to obtain four-dimensional feature vectors
Figure FDA00036019072500000214
The method specifically comprises the following steps:
processing four-dimensional feature vectors using global maximum pooling
Figure FDA00036019072500000215
And obtaining the feature vector
Figure FDA00036019072500000216
The formula is as follows:
Figure FDA0003601907250000031
wherein, Pmax(. is a global maximum pooling function, Mc×2×2Carrying out pixel-by-pixel operation on the convolution kernel characteristic of the filter k-2, selecting the maximum value and outputting the maximum value;
processing feature vectors using full join functions
Figure FDA0003601907250000032
And obtaining a feature vector
Figure FDA0003601907250000033
The formula is as follows:
Figure FDA0003601907250000034
wherein, Cfully(. for) a full connection function, using Mc×1×1I.e. the convolution kernel of filter k ═ 1 operates;
feature vector using deep convolutional neural network
Figure FDA0003601907250000035
Uniformly dividing the characteristic channel into p branches (p is less than or equal to c) to obtain the characteristic component of each characteristic channel
Figure FDA0003601907250000036
The formula is as follows:
Figure FDA0003601907250000037
wherein, Fdeep() is a deep convolutional neural network process function;
randomly exchanging features by dividing each branch into q groups, and disturbing information between different channelsThe characteristic information is reclassified and combined in sequence to obtain characteristic components
Figure FDA0003601907250000038
The formula is as follows:
Figure FDA0003601907250000039
wherein, Shuffle (-) is an information exchange function;
using point convolution neural network to feature components
Figure FDA00036019072500000310
Merging to obtain four-dimensional characteristic vector
Figure FDA00036019072500000311
The point convolution neural network randomly deletes part of neurons in the merging process, and the formula is as follows:
Figure FDA00036019072500000312
wherein D isranIs a random deletion function, and m is the proportion of randomly deleted neurons;
Figure FDA00036019072500000313
wherein, Fpoi(. to) a point convolution neural network process function, using Mc×1×1A formal point convolution performs a point convolution operation on the feature vector.
6. The method of claim 1, wherein the feature vector is further processed using multi-layer residual units
Figure FDA00036019072500000314
Obtaining four-dimensional feature vectors
Figure FDA00036019072500000315
The method specifically comprises the following steps:
Figure FDA00036019072500000316
wherein the content of the first and second substances,
Figure FDA0003601907250000041
is a weight matrix.
7. The method of claim 1, wherein the feature vector is used to transfer the content feature transfer style
Figure FDA0003601907250000042
And four-dimensional feature vectors
Figure FDA0003601907250000043
Multiplying to generate four-dimensional content feature vector Y1 c×h×wThe method specifically comprises the following steps:
Figure FDA0003601907250000044
wherein the content of the first and second substances,
Figure FDA0003601907250000045
and
Figure FDA0003601907250000046
for the weight matrix, x represents the feature matrix multiplication.
8. A semantic-guided content feature transfer style migration system is characterized by comprising an encoding module, a content calibration module and a decoding module;
the encoding module comprises a content encoding module and a style encoding module; the content encoding module inputs source domain input images
Figure FDA0003601907250000047
As input, double down sampling operation is performed on the four-dimensional feature vector to output the four-dimensional feature vector
Figure FDA0003601907250000048
The style coding module inputs the target domain into the image
Figure FDA0003601907250000049
As input, the four-dimensional feature vector is output by sequentially using global average pooling, full-connected function and style cosine normalization processing
Figure FDA00036019072500000410
The content calibration module comprises a feature optimization unit and an attribute reasoning unit; the characteristic optimization unit is used for down-sampling the source domain input image
Figure FDA00036019072500000411
Obtaining four-dimensional characteristic vector by adopting multi-layer residual error unit processing
Figure FDA00036019072500000412
The attribute reasoning unit pairs four-dimensional feature vectors
Figure FDA00036019072500000413
Sequentially carrying out global maximum pooling, full-connection function processing, deep convolutional neural network, information exchange and point convolutional neural network processing to obtain four-dimensional feature vectors
Figure FDA00036019072500000414
Simultaneous use of multilayer residuesFeature vector deepening processing of difference unit
Figure FDA00036019072500000415
Obtaining four-dimensional feature vectors
Figure FDA00036019072500000416
Combining four-dimensional feature vectors
Figure FDA00036019072500000417
And four-dimensional feature vector
Figure FDA00036019072500000418
In a fixed ratio omega1And omega2Multiplying and outputting four-dimensional feature vector Y1 c×h×w；
The decoding module is used for decoding the four-dimensional feature vector
Figure FDA00036019072500000419
And a four-dimensional feature vector Y1 c×h×wAdding and fusing to obtain four-dimensional feature vector
Figure FDA00036019072500000420
Then, the style migration result Y is output by up-samplingc×2h×2w。
9. The system of claim 8, wherein the content alignment module is expressed as:
Figure FDA00036019072500000421
wherein, Fopt(x) Learning a process function for the feature optimization unit, Fre(x) A process function is learned for the attribute reasoning unit.
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