Abstract: Systems and methods for image segmentation using neural networks and active contour methods in accordance with embodiments of the invention are illustrated. One embodiment includes a method for generating image segmentations from an input image. The method includes steps for receiving an input image, identifying a set of one or more parameter maps from the input image, identifying an initialization map from the input image, and generating an image segmentation based on the set of parameter maps and the initialization map.

Abstract: Vision transformers are deep learning models that employ a self-attention mechanism to obtain feature representations for an input image. To date, the configuration of vision transformers has limited the self-attention computation to a local window of the input image, such that short-range dependencies are modeled in the output. The present disclosure provides a vision transformer that captures global context, and that is therefore able to model long-range dependencies in its output.

Abstract: Apparatuses, systems, and techniques to train a neural network to infer a condition based on an image. In at least one embodiment, a first portion of a neural network is trained to infer a condition from an image using a first dataset, and a second portion of the neural network is trained using a second dataset.

Abstract: Apparatuses, systems, and techniques are presented to predict annotations for objects in images. In at least one embodiment, one or more neural networks are used to help generate one or more segmentation boundaries of one or more objects within one or more digital images, wherein the one or more neural networks are to transform one or more representations of one or more portions of the one or more objects into one or more lower-dimensional representations of the one or more portions of the one or more objects.

Abstract: A boundary learning optimization tool for training neural networks with accurate models of parameterized flexure using a minimal number of numerically generated performance solutions generated from different design instantiations of those topologies. Performance boundaries are output by the neural network in optimization steps, with geometric parameters varied from smallest allowable feature sizes to largest geometrically compatible feature sizes for given constituent materials. The plotted performance boundaries define the design spaces of flexure systems toward allowing designers to visually identify which geometric versions of their synthesized topologies best achieve desired combinations of performance capabilities.

Abstract: Systems and methods for image segmentation using neural networks and active contour methods in accordance with embodiments of the invention are illustrated. One embodiment includes a method for generating image segmentations from an input image. The method includes steps for receiving an input image, identifying a set of one or more parameter maps from the input image, identifying an initialization map from the input image, and generating an image segmentation based on the set of parameter maps and the initialization map.