METHOD FOR TRAINING A MODEL TO BE USED FOR PROCESSING IMAGES BY GENERATING FEATURE MAPS
A method for training a model to be used for processing images, wherein the model comprises: —a first portion ( 101 ) configured to receive images as input and configured to output a feature map, —a second portion ( 102 ) configured to receive the feature map outputted by the first portion as input and configured to output a semantic segmentation, the method comprising: —training a generator ( 201 ) so that the generator is configured to generate a feature map configured to be used as input to the second portion, —generating a plurality of feature maps using the generator, —training the second portion using the feature maps generated by the generator.
1 . A method for training a model to be used for processing images, wherein the model comprises:
a first portion configured to receive images as input and configured to output a feature map,
a second portion configured to receive the feature map outputted by the first portion as input and configured to output a semantic segmentation,
the method comprising:
training a generator so that the generator is configured to generate a feature map configured to be used as input to the second portion,
generating a plurality of feature maps using the generator,
training the second portion using the feature maps generated by the generator.
2 . The method of claim 1 , wherein the generator is trained with an adversarial training.
3 . The method of claim 1 , comprising a preliminary training of the model using a set of images and, for each image of the set of image, a predefined processed image.
4 . The method of claim 3 , wherein training the generator comprises using the predefined processed images as input to the generator.
5 . The method of claim 3 , wherein training the generator comprises using processed images obtained using the model on images from the set of images.
6 . The method of claim 3 , wherein training the generator comprises using feature maps obtained using the first portion on images from the set of images.
7 . The method according to claim 1 , wherein training the generator comprises inputting an additional random variable as input to the generator.
8 . The method according to claim 1 , wherein the generator comprises a module configured to adapt the output dimensions of the generator to the input size of the second portion.
9 . The method according to claim 1 , wherein the generator comprises a convolutional network.
10 . The method according to claim 2 , wherein training the generator with an adversarial training comprises using a discriminator receiving a processed image as input, the discriminator comprising a module configured to adapt the dimensions of the processed image to be used as input.
11 . The method according to claim 10 , wherein the discriminator comprises a convolutional neural network.
12 . The method according to claim 1 , comprising determining a loss taking into account the output of the model for an image and the output of the second portion for a feature map generated by the generator, determining the loss comprising performing a smoothing.
13 . The method according to claim 1 , wherein the model is a model to be used for semantic segmentation of images.
14 . The method according to claim 1 , wherein the model comprises a module configured to output a processed image by taking into account:
A: the output of the second portion for a feature map obtained with the first portion on an image,
B: the output of the second portion for a feature map obtained with the generator using A as input to the generator.
15 . A system for training a model to be used for processing images, wherein the model comprises:
a first portion configured to receive images as input and configured to output a feature map,
a second portion configured to receive the feature map outputted by the first portion as input and configured to output a processed image,
the system comprising:
a module for training a generator so that the generator is configured to generate a feature map configured to be used as input to the second portion,
a module for generating a plurality of feature maps using the generator,
a module for training the second portion using the feature maps generated by the generator.
16 . A model to be used for processing images, wherein the model comprises:
a first portion configured to receive images as input and configured to output a feature map,
a second portion configured to receive the feature map outputted by the first portion as input and configured to output a semantic segmentation, and
the model has been trained by:
training a generator so that the generator is configured to generate a feature map configured to be used as input to the second portion,
generating a plurality of feature maps using the generator,
training the second portion using the feature maps generated by the generator.
17 . A system for processing images, comprising an image acquisition module and the model according to claim 16 .
18 . A vehicle comprising a system according to claim 17 .
19 . (canceled)
20 . A non-transitory recording medium readable by a computer and having recorded thereon a computer program including instructions that when executed by a processor cause the processor to train a model to be used for processing images, wherein the model comprises:
a first portion configured to receive images as input and configured to output a feature map,
a second portion configured to receive the feature map outputted by the first portion as input and configured to output a semantic segmentation,
the training comprising:
training a generator so that the generator is configured to generate a feature map configured to be used as input to the second portion,
generating a plurality of feature maps using the generator,
training the second portion using the feature maps generated by the generator.