Abstract: Methods, systems, and non-transitory computer readable storage media are disclosed for generating difference captions indicating detected differences in digital image pairs. The disclosed system generates a first feature map of a first digital image and a second feature map of a second digital image. The disclosed system converts, utilizing a linear projection neural network, the first feature map to a first modified feature map in a feature space corresponding to a large language machine-learning model. The disclosed system also converts, utilizing the linear projection neural network layer, the second feature map to a second modified feature map in the feature space corresponding to the large language machine-learning model.

Abstract: This disclosure describes one or more implementations of systems, non-transitory computer-readable media, and methods that trains (and utilizes) an image color editing diffusion neural network to generate a color edited digital image(s) for a digital image. In particular, in one or more implementations, the disclosed systems identify a digital image depicting content in a first color style. Moreover, the disclosed systems generate, from the digital image utilizing an image color editing diffusion neural network, a color-edited digital image depicting the content in a second color style different from the first color style. Further, the disclosed systems provide, for display within a graphical user interface, the color-edited digital image.

Abstract: Digital content stylization techniques are described that leverage a neural photofinisher to generate stylized digital images. In one example, the neural photofinisher is implemented as part of a stylization system to train a neural network to perform digital image style transfer operations using reference digital content as training data. The training includes calculating a style loss term that identifies a particular visual style of the reference digital content. Once trained, the stylization system receives a digital image and generates a feature map of a scene depicted by the digital image. Based on the feature map as well as the style loss, the stylization system determines visual parameter values to apply to the digital image to incorporate a visual appearance of the particular visual style. The stylization system generates the stylized digital image by applying the visual parameter values to the digital image automatically and without user intervention.

Abstract: Embodiments are disclosed for predicting a user's intent to generate a masked region of an image based on a user gesture. The method may include receiving a user interaction corresponding to a region in an image. The user interaction is used to select the object in the image. The method further includes generating a feature map using a first machine learning model and the user interaction, where the feature map includes at least one feature corresponding to a mode type. The mode type is an intended operation of a user. Lastly, the method includes generating a mask of the object in the image using a second machine learning model, wherein the mask is generated responsive to the mode type.

Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for utilizing machine learning models to generate refined depth maps of digital images utilizing digital segmentation masks. In particular, in one or more embodiments, the disclosed systems generate a depth map for a digital image utilizing a depth estimation machine learning model, determine a digital segmentation mask for the digital image, and generate a refined depth map from the depth map and the digital segmentation mask utilizing a depth refinement machine learning model. In some embodiments, the disclosed systems generate first and second intermediate depth maps using the digital segmentation mask and an inverse digital segmentation mask and merger the first and second intermediate depth maps to generate the refined depth map.

Abstract: A walking assistant robot, comprising a main frame body (1), a foot walking mechanism for alternating walking, an auxiliary device (2) and a control device; the walking mechanism, the auxiliary device (2) and the control device are all disposed on the main frame body (1); the control device is connected with the walking mechanism; the auxiliary device (2) includes auxiliary feet (201), and the auxiliary feet (201) are disposed in front of and/or behind the walking mechanism along the walking direction. The foot walking mechanism is used to realize the basic function of the robot walking forward; auxiliary feet (201) are disposed in the walking direction such that the walking mechanism still has, at the moment of foot alternation, multiple supporting points in contact with the ground. Therefore, the mechanism is slip-and-fall-resistant, stable and reliable and guarantees the user safety.

Abstract: A walking assistant robot, comprising a main frame body (1), a foot walking mechanism for alternating walking, an auxiliary device (2) and a control device; the walking mechanism, the auxiliary device (2) and the control device are all disposed on the main frame body (1); the control device is connected with the walking mechanism; the auxiliary device (2) includes auxiliary feet (201), and the auxiliary feet (201) are disposed in front of and/or behind the walking mechanism along the walking direction. The foot walking mechanism is used to realize the basic function of the robot walking forward; auxiliary feet (201) are disposed in the walking direction such that the walking mechanism still has, at the moment of foot alternation, multiple supporting points in contact with the ground. Therefore, the mechanism is slip-and-fall-resistant, stable and reliable and guarantees the user safety.