Abstract: An example system includes a processor to receive, a randomly generated alpha-map, a pair of training images, and a pair of training texts associated with the pair of training images. The processor is to generate a blended image based on the randomly generated alpha-map and the pair of training images. The processor is to train a visual language grounding model to separate the blended image into a pair of heatmaps identifying portions of the blended image corresponding to each of the training images using a separation loss.

Abstract: An example a system includes a processor to receive a model that is a neural network and a number of training images. The processor can train the model using a bridge transform that converts the training images into a set of transformed images within a bridge domain. The model is trained using a contrastive loss to generate representations based on the transformed images.

Abstract: An example system includes a processor to receive, a randomly generated alpha-map, a pair of training images, and a pair of training texts associated with the pair of training images. The processor is to generate a blended image based on the randomly generated alpha-map and the pair of training images. The processor is to train a visual language grounding model to separate the blended image into a pair of heatmaps identifying portions of the blended image corresponding to each of the training images using a separation loss.

Abstract: A method for multiple sensor calibration and tracking, the method including identifying a non-permanent object in a first field of detection (FOD) of a sensor, wherein the sensor is one of a plurality of sensors positioned in various locations at a site, each sensor has a corresponding FOD; tracking the identified object by single-sensor tracking in the first FOD; predicting appearance of the object, and based on the predictions initiating single-sensor tracking in at least one other FOD where it is predicted that the object will appear; and outputting data captured from the tracked FODs.

Abstract: A method for multiple sensor calibration and tracking, the method including identifying a non-permanent object in a first field of detection (FOD) of a sensor, wherein the sensor is one of a plurality of sensors positioned in various locations at a site, each sensor has a corresponding FOD; tracking the identified object by single-sensor tracking in the first FOD; predicting appearance of the object, and based on the predictions initiating single-sensor tracking in at least one other FOD where it is predicted that the object will appear; and outputting data captured from the tracked FODs.

Abstract: A method of controlling output of a neural network, the method including receiving or training the neural network; wherein the neural network is an application executed on a computer that receives input from sensors and provides an output comprising predictions and/or decisions based on the input, identifying a region of the neural network that contains information of interest, finding within the identified region a specific node or group of nodes that contains specific information of interest; and applying a manipulation application external to the neural network to operate on and alter the output of the specific node or group of nodes within the neural network; wherein the altered output of the specific node affects the output of the neural network without altering the input of the neural network.