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Found 6 papers, 2 papers with code
RealCompo: Balancing Realism and Compositionality Improves Text-to-Image Diffusion Models
In this paper, we propose RealCompo, a new training-free and transferred-friendly text-to-image generation framework, which aims to leverage the respective advantages of text-to-image models and spatial-aware image diffusion models (e. g., layout, keypoints and segmentation maps) to enhance both realism and compositionality of the generated images.
Privileged Prior Information Distillation for Image Matting
Performance of trimap-free image matting methods is limited when trying to decouple the deterministic and undetermined regions, especially in the scenes where foregrounds are semantically ambiguous, chromaless, or high transmittance.
L-MAE: Masked Autoencoders are Semantic Segmentation Datasets Augmenter
In detail, L-MAE adopts the fusion strategy that stacks the label and the corresponding image, namely fuse map.
Situational Perception Guided Image Matting
In this paper, we propose a Situational Perception Guided Image Matting (SPG-IM) method that mitigates subjective bias of matting annotations and captures sufficient situational perception information for better global saliency distilled from the visual-to-textual task.
Prior-Induced Information Alignment for Image Matting
Image matting is an ill-posed problem that aims to estimate the opacity of foreground pixels in an image.
Tripartite Information Mining and Integration for Image Matting
Regarding the nature of image matting, most researches have focused on solutions for transition regions.
