![]() ![]() reduce_mean( grad_alpha * grad_error)Įdge_weight = edge_alpha * edge_priority # print("edge_weight", edge_weight.shape) # print("grad_error", grad_error.shape) edge_loss = tf. float32, stateful = False)Įdge_priority = priority_loss_mask( gt_edge, ksize = 5, sigma = 1, iteration = 2) reshape( gt_grad, ) # 6 channel grad_error = tf. resize_nearest_neighbor( image, size =( h, w)) Here, the gradient and edge which are holded in sobel gradient maps as in figure below are used as the structure information.ĭef pyramid_structure_loss( image, predicts, edge_alpha, grad_alpha): We propose a pyramid structure loss to guide the structure generation and embedding, thus incorporating the structure information into the generation process. It leverages the structure knowledge with multi-tasking learning (simultaneous image and structure generation), structure embedding and attention mechanism. The overview of our multi-task framework is as in figure below. Through multi-task learning, structure embedding besides with attention, our framework takes advantage of the structure knowledge and outperforms several state-of-the-art methods on benchmark datasets quantitatively and qualitatively. Moreover, an attention mechanism is developed to further exploit the recurrent structures and patterns in the image to refine the generated structures and contents. Specifically, a novel pyramid structure loss is proposed to supervise structure learning and embedding. In the meantime, we also introduce a structure embedding scheme to explicitly embed the learned structure features into the inpainting process, thus to provide possible preconditions for image completion. The primary idea is to train a shared generator to simultaneously complete the corrupted image and corresponding structures - edge and gradient, thus implicitly encouraging the generator to exploit relevant structure knowledge while inpainting. This project develops a multi-task learning framework that attempts to incorporate the image structure knowledge to assist image inpainting, which is not well explored in previous works. To Incorporate Structure Knowledge for Image Inpainting},īooktitle=, ![]()
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