Abstract: An AI based change detection system for executing a method to detect changes in geo-tagged videos to update HD maps, the method employing a neural network of modular components including a keyframe extraction module for processing two or more videos relating to separate traversals of an area of interest to which the HD map which is to be updated relates, a deep neural network module processing output of the keyframe extraction module, a change detection module processing output of the deep neural network module, and an auxiliary computations module which is designed to aid the change detection module.

Abstract: A computer-implemented method that provides a novel shape aware FSL framework, referred to as LSFSL. In addition to the inductive biases associated with deep learning models, the method of the current invention introduces meaningful shape bias. The method of the current invention comprises the step of capturing the human behavior of recognizing objects by utilizing shape information. The shape information is distilled to address the texture bias of CNN-based models. During training, the model has two branches: RIN-branch, network with colored images as input, preferably RGB images, and SIN-branch, network with shape semantic-based input. Each branch incorporates a CNN backbone followed by a fully connected layer performing classification. RIN-branch and SIN-branch receive the RGB input image and shape information enhanced RGB input image, respectively.

Abstract: A computer-implemented method for continual task learning in an artificial cognitive architecture that includes a first neural network module for encoding explicit knowledge representations, a second neural network module for encoding implicit knowledge representations, and a memory buffer. A visual data stream is provided to the architecture. Visual data samples are stored from said visual data stream in the memory buffer. Both visual data samples of the visual data stream and visual data samples from the memory buffer are processed using the first neural network module for learning explicit knowledge representations. Both samples of said visual data stream and visual data samples from the memory buffer are processed using the second neural network module for learning implicit knowledge representations.

Abstract: A computer-implemented method of self-supervised learning for deep neural networks including the steps of: providing input images (x); extracting implicit shape information from the input images; and performing self-supervised learning on at least two deep neural network (f) based on the provided input images (x) and the at least one extracted implicit shape information for enabling said at least one deep neural network (f) to classify and/or detect objects within other input images.

Abstract: A computer implemented method to distill an inductive bias in a deep neural network operating on image data, the deep neural network comprising a standard network that receives original images from the image data, and an inductive-bias network that receives shape data of the images, and a bias alignment is performed on the standard network and inductive-bias network in feature space and decision space to enable the networks to learn both local texture information and global shape information to produce high-level, generic representations.

Abstract: An AI based change detection system for executing a method to detect changes in geo-tagged videos to update HD maps, the method employing a neural network of modular components including a keyframe extraction module for processing two or more videos relating to separate traversals of an area of interest to which the HD map which is to be updated relates, a deep neural network module processing output of the keyframe extraction module, a change detection module processing output of the deep neural network module, and an auxiliary computations module which is designed to aid the change detection module.

Abstract: A Deep Learning based Multi-sensor Detection System for executing a method to process images from a visual sensor and from a thermal sensor for detection of objects in said images, wherein a first deep learning network for processing images from the visual sensor and a second deep learning network for pro-cessing images from the thermal sensor are jointly used and collaboratively trained for improving both networks ability to accurately detect said objects in said images.