Abstract: A method for training an image colorization model may include inputting a training input image into a colorization model and receive a predicted color map as an output of the colorization model. A first color distance may be calculated between a first pixel of the predicted color map and a second pixel of the predicted color map. A second color distance may be calculated between a third pixel included in a ground truth color map and a fourth pixel included in the ground truth colorization map. The third pixel and fourth pixel included in the ground truth color map may spatially correspond, respectively, with the first pixel and second pixel included in the predicted color map. The method may include adjusting parameters associated with the colorization model based on a neighborhood color loss function that evaluates a difference between the first color distance and the second color distance.

Abstract: A method includes determining a mask defining a portion of a perceptual representation, convolutional features associated with the perceptual representation, and contextual attention features associated with the perceptual representation. The method also includes determining a concatenation of the mask, the convolutional features, and the contextual attention features. The method additionally includes determining gate values for the portion, where determining gate values includes processing the concatenation using a machine learning model. The method further includes determining gated convolutional features based on the convolutional features and the gate values and gated contextual attention features based on the contextual attention features and the gate values. The method yet further includes generating refined values for the portion based on the gated convolutional features and the gated contextual attention features.

Abstract: A system or method for inpainting can be aided through the use of machine learning and ground truth data training. The training of machine-learning inpainting models through the use of ground truth image data may add efficiency and precision to the field of image inpainting. Furthermore, machine-learning inpainting models can aid in the non-deterministic prediction of a variety of data types and can be applicable to the removing and/or replacing of a variety of data types. The trained models can be enabled to make predictions without ground truth reassurance due to calibrated parameters tuned through the training.

Abstract: Systems and methods for augmenting images can utilize one or more image augmentation models and one or more texture transfer blocks. The image augmentation model can process input images and one or more segmentation masks to generate first output data. The first output data and the one or more segmentation masks can be processed with the texture transfer block to generate an augmented image. The input image can depict a scene with one or more occlusions, and the augmented image can depict the scene with the one or more occlusions replaced with predicted pixel data.

Abstract: The present disclosure provides systems and methods that leverage neural networks for high resolution image segmentation. A computing system can include a processor, a machine-learned image segmentation model comprising a semantic segmentation neural network and an edge refinement neural network, and at least one tangible, non-transitory computer readable medium that stores instructions that cause the processor to perform operations. The operations can include obtaining an image, inputting the image into the semantic segmentation neural network, receiving, as an output of the semantic segmentation neural network, a semantic segmentation mask, inputting at least a portion of the image and at least a portion of the semantic segmentation mask into the edge refinement neural network, and receiving, as an output of the edge refinement neural network, the refined semantic segmentation mask.

Abstract: A method for training an image colorization model may include inputting a training input image into a colorization model and receive a predicted color map as an output of the colorization model. A first color distance may be calculated between a first pixel of the predicted color map and a second pixel of the predicted color map. A second color distance may be calculated between a third pixel included in a ground truth color map and a fourth pixel included in the ground truth colorization map. The third pixel and fourth pixel included in the ground truth color map may spatially correspond, respectively, with the first pixel and second pixel included in the predicted color map. The method may include adjusting parameters associated with the colorization model based on a neighborhood color loss function that evaluates a difference between the first color distance and the second color distance.

Abstract: The present disclosure provides systems and methods that leverage neural networks for high resolution image segmentation. A computing system can include a processor, a machine-learned image segmentation model comprising a semantic segmentation neural network and an edge refinement neural network, and at least one tangible, non-transitory computer readable medium that stores instructions that cause the processor to perform operations. The operations can include obtaining an image, inputting the image into the semantic segmentation neural network, receiving, as an output of the semantic segmentation neural network, a semantic segmentation mask, inputting at least a portion of the image and at least a portion of the semantic segmentation mask into the edge refinement neural network, and receiving, as an output of the edge refinement neural network, the refined semantic segmentation mask.

Abstract: The present disclosure provides systems and methods that leverage neural networks for high resolution image segmentation. A computing system can include a processor, a machine-learned image segmentation model comprising a semantic segmentation neural network and an edge refinement neural network, and at least one tangible, non-transitory computer readable medium that stores instructions that cause the processor to perform operations. The operations can include obtaining an image, inputting the image into the semantic segmentation neural network, receiving, as an output of the semantic segmentation neural network, a semantic segmentation mask, inputting at least a portion of the image and at least a portion of the semantic segmentation mask into the edge refinement neural network, and receiving, as an output of the edge refinement neural network, the refined semantic segmentation mask.

Abstract: The present disclosure provides systems and methods that leverage neural networks for high resolution image segmentation. A computing system can include a processor, a machine-learned image segmentation model comprising a semantic segmentation neural network and an edge refinement neural network, and at least one tangible, non-transitory computer readable medium that stores instructions that cause the processor to perform operations. The operations can include obtaining an image, inputting the image into the semantic segmentation neural network, receiving, as an output of the semantic segmentation neural network, a semantic segmentation mask, inputting at least a portion of the image and at least a portion of the semantic segmentation mask into the edge refinement neural network, and receiving, as an output of the edge refinement neural network, the refined semantic segmentation mask.