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 to improve scale consistency and/or scale awareness in a model of self-supervised depth and ego-motion prediction neural networks processing a video stream of monocular images, wherein complementary GPS coordinates synchronized with the images are used to calculate a GPS to scale loss to enforce the scale-consistency and/or -awareness on the monocular self-supervised ego-motion and depth estimation. A relative weight assigned to the GPS to scale loss exponentially increases as training progresses. The depth and ego-motion prediction neural networks are trained using an appearance-based photometric loss between real and synthesized target images, as well as a smoothness loss on the depth predictions.

Abstract: Systems arranged to implement methods for positioning a semantic landmark in an image from the real world during a continuous motion of a monocular camera providing said image, using in combination image information from the camera and GPS information, wherein the camera parameters are unknown a priori and are estimated in a self-calibration step, wherein in a subsequent step positioning of the landmarks is completed using one of camera ego motion and depth estimation.

Abstract: A semantic segmentation architecture comprising an asymmetric encoder—decoder structure, wherein the architecture comprises further an adapter for linking different stages of the encoder and the decoder. The adapter amalgamates information from both the encoder and the decoder for preserving and refining information between multiple levels of the encoder and decoder. In this way the adapter aggregates features from different levels and intermediates between encoder and decoder.

Abstract: Embodiments of the disclosure provide methods and systems for an artificial intelligence method of making predictions from a sequence of images. The method may include receiving the sequence of images acquired at different time points. The method may further include applying a stable model to process the sequence of images to make the predictions. The stable model is trained along with a working model and a plastic model. The training enforces a consistency among the working model, the stable model, and the plastic model. The working model is trained using a loss function including a cross-entropy loss on a union of a training batch and memory exemplars and a consistency loss on the memory exemplars.

Abstract: A semantic segmentation architecture comprising an asymmetric encoder-decoder structure, wherein the architecture comprises further an adapter for linking different stages of the encoder and the decoder. The adapter amalgamates information from both the encoder and the decoder for preserving and refining information between multiple levels of the encoder and decoder. In this way the adapter aggregates features from different levels and intermediates between encoder and decoder.

Abstract: Real-time scene understanding system employing an object detection module with an algorithm for localization and classification of objects in an image, and a semantic segmentation module with an algorithm for classification of individual pixels in the image, wherein the system comprises an encoder module operable on an input image for the extraction of notable features in the input image, one or more attention modules to attribute among the notable features in the input image as provided by the encoder a relative contribution of each of such notable features in an output image to be reconstructed from the input image, and a decoder module for reconstructing the output image using the notable features, wherein the reconstructed output image is made available to the object detection module with the algorithm for localization and classification of objects in the image, and to the semantic segmentation module with the algorithm for classification of individual pixels in the image.