2 citations · 2 across the 5 of their papers we have counts for
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IterComp: Iterative Composition-Aware Feedback Learning from Model Gallery for Text-to-Image Generation
Xinchen Zhang, Ling Yang, Guohao Li +6
Advanced diffusion models like RPG, Stable Diffusion 3 and FLUX have made notable strides in compositional text-to-image generation. However, these methods typically exhibit distin…
RealCompo: Balancing Realism and Compositionality Improves Text-to-Image Diffusion Models
Xinchen Zhang, Ling Yang, Yaqi Cai +8
Diffusion models have achieved remarkable advancements in text-to-image generation. However, existing models still have many difficulties when faced with multiple-object compositio…
Privileged Prior Information Distillation for Image Matting
Cheng Lyu, Jiake Xie, Bo Xu +6
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 seman…
L-MAE: Masked Autoencoders are Semantic Segmentation Datasets Augmenter
Jiaru Jia, Mingzhe Liu, Jiake Xie +4
Generating semantic segmentation datasets has consistently been laborious and time-consuming, particularly in the context of large models or specialized domains(i.e. Medical Imagin…
Situational Perception Guided Image Matting
Bo Xu, Jiake Xie, Han Huang +4
Most automatic matting methods try to separate the salient foreground from the background. However, the insufficient quantity and subjective bias of the current existing matting da…
Prior-Induced Information Alignment for Image Matting
Yuhao Liu, Jiake Xie, Yu Qiao +2
Image matting is an ill-posed problem that aims to estimate the opacity of foreground pixels in an image. However, most existing deep learning-based methods still suffer from the c…