Competitors, page-14

They are big on CNN.

US10311342 (Patent)
Claim 1. A method for processing an image, comprising:by a camera of a mobile computing device, capturing the image;by a processor of the mobile computing device, inputting data representing the image into a convolutional neural network (CNN), the CNN including a plurality of convolutional layers, a set of weights or filters for at least one of the layers, and a set of input data to the at least one of the layers;by the processor of the mobile computing device, representing a convolution operation between the set of input data and the set of filters or weights by a product of a scaling factor and a binary representation of the set of filters or weights convolved with the set of input data, wherein the binary representation is the sign of the weight values, and the scaling factor is the average of the absolute weight values; andby the processor of the mobile computing device, applying a classification operation to an output of the last of the plurality of convolutional layers.

US2019102646 (Application)
Abstract: Systems and methods are disclosed for image-based object detection and classification. For example, methods may include accessing an image from an image sensor; applying a convolutional neural network to the image to obtain localization data to detect an object depicted in the image and to obtain classification data to classify the object, in which the convolutional neural network has been trained in part using training images with associated localization labels and classification labels and has been trained in part using training images with associated classification labels that lack localization labels; annotating the image based on the localization data and the classification data to obtain an annotated image; and storing, displaying, or transmitting the annotated image.

US2019026600 (Application)
Systems and methods are disclosed for lookup-based convolutional neural networks. For example, methods may include applying a convolutional neural network to image data based on an image to obtain an output, in which a layer of the convolutional network includes filters with weights that are stored as a dictionary (D) of channel weight vectors, a respective lookup index tensor (I) that indexes the dictionary, and a respective lookup coefficient tensor (C), and in which applying the convolutional neural network includes: convolving the channel weight vectors of the dictionary (D) with an input tensor based on the image to obtain an input dictionary (S), and combining entries of the input dictionary (S) that are indexed with indices from the respective lookup index tensor (I) and multiplied with corresponding coefficients from the respective lookup coefficient tensor (C); and storing, displaying, or transmitting data based on the output of the convolutional neural network.