According to GlobalData’s company profile on TomTom, AV localization algorithms was a key innovation area identified from patents. TomTom's grant share as of June 2023 was 1%. Grant share is based on the ratio of number of grants to total number of patents.

The patent is filed for a computer system for semantic image segmentation

A recently filed patent (Publication Number: US20230186100A1) describes a computer system that is capable of training a model for semantic image segmentation. The model consists of a discriminator neural network and a refinement neural network. The refinement neural network receives a predicted label distribution for an image and generates multiple predicted segmentation maps using random values obtained from a noise source. These predicted segmentation maps are then passed to the discriminator neural network. The computer system is configured to train the refinement neural network using an objective function that includes a term representing the difference between the predicted label distribution and the average of the predicted segmentation maps.

The computer system described in the patent is designed to implement the model for semantic image segmentation. It includes one or more processors that are responsible for training the refinement neural network and the discriminator neural network. The refinement neural network receives the predicted label distribution from a calibration neural network, which is also part of the model. The calibration neural network takes input images and produces predicted label distributions using a likelihood-based semantic segmentation approach. The computer system is capable of training both the calibration neural network and the discriminator neural network.

The training process for the refinement neural network and the discriminator neural network involves a generative-adversarial-network (GAN) training process. The computer system conditions both neural networks on the input image during training. The discriminator neural network is trained using a loss function that measures its ability to discriminate between the predicted segmentation maps generated by the refinement network and ground-truth label data.

The objective function used to train the refinement neural network is a combination of two terms. The first term depends on the output of the discriminator network, while the second term depends on the difference between the predicted label distribution and the average of the predicted segmentation maps generated by the refinement network. The average of the predicted segmentation maps is calculated by taking the pixel-wise arithmetic mean of the multiple predicted segmentation maps.

The computer system employs a gradient descent process to train any of the neural networks involved in the model. The patent also describes a method for training the model, as well as a non-transitory computer-readable storage medium that stores instructions for implementing the training and implementation of the model for semantic image segmentation.

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