Abstract: Directional propagation editing techniques are described, in one example, a digital image, a depth map, and a direction are obtained by an image editing system. The image editing system then generates features. To do so, the image editing system generates features from the digital image and the depth map for each pixel based on the direction, e.g., until an edge of the digital image is reached. In an implementation, instead of storing a value of the depth directly, a ratio is stored based on a depth in the depth map and a depth of a point along the direction. The image editing system then forms a feature volume using the features, e.g., as three dimensionally stacked features. The feature volume is employed by the image editing system as part of editing the digital image to form an edited digital image.

Abstract: A computer-implemented method of training an object detector, the method comprising: training an embedding neural network using, as an input, cropped images from an image dataset, wherein training the embedding neural network is performed using a self-supervised learning approach and the trained embedding neural network translates input images into a lower dimensional representation; and training an object detector neural network by, for images of the image dataset, repeatedly: passing an image through the object detector neural network to obtain proposed coordinates of an object within the image, cropping the image to the proposed coordinates to obtain a cropped image, passing the cropped image through the trained embedding neural network to obtain a cropped image representation, passing an exemplar through the trained embedding neural network to obtain an exemplar representation, wherein the exemplar is a cropped manually labelled image bounding a known object, computing a distance in embedding space betwee

Abstract: Directional propagation editing techniques are described, in one example, a digital image, a depth map, and a direction are obtained by an image editing system. The image editing system then generates features. To do so, the image editing system generates features from the digital image and the depth map for each pixel based on the direction, e.g., until an edge of the digital image is reached. In an implementation, instead of storing a value of the depth directly, a ratio is stored based on a depth in the depth map and a depth of a point along the direction. The image editing system then forms a feature volume using the features, e.g., as three dimensionally stacked features. The feature volume is employed by the image editing system as part of editing the digital image to form an edited digital image.