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Found 4 papers, 3 papers with code
Structure Matters: Tackling the Semantic Discrepancy in Diffusion Models for Image Inpainting
Denoising diffusion probabilistic models for image inpainting aim to add the noise to the texture of image during the forward process and recover masked regions with unmasked ones of the texture via the reverse denoising process. Despite the meaningful semantics generation, the existing arts suffer from the semantic discrepancy between masked and unmasked regions, since the semantically dense unmasked texture fails to be completely degraded while the masked regions turn to the pure noise in diffusion process, leading to the large discrepancy between them.
Fine-grained Cross-modal Fusion based Refinement for Text-to-Image Synthesis
The proposed FF-Block integrates an attention block and several convolution layers to effectively fuse the fine-grained word-context features into the corresponding visual features, in which the text information is fully used to refine the initial image with more details.
Deep Cost-sensitive Learning for Wheat Frost Detection
Experimental results show that the detection accuracy and the score reached 0. 943 and 0. 623 respectively, this demonstration shows that this method not only ensures the overall accuracy but also effectively improves the detection rate of frost samples.
Delving Globally into Texture and Structure for Image Inpainting
Our model is orthogonal to the fashionable arts, such as Convolutional Neural Networks (CNNs), Attention and Transformer model, from the perspective of texture and structure information for image inpainting.
