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pro vyhledávání: '"Xie, Jiake"'
Autor:
Zhang, Xinchen, Yang, Ling, Li, Guohao, Cai, Yaqi, Xie, Jiake, Tang, Yong, Yang, Yujiu, Wang, Mengdi, Cui, Bin
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 distinct strengths for compositional generation, with some excelling in hand
Externí odkaz:
http://arxiv.org/abs/2410.07171
Autor:
Zhang, Xinchen, Yang, Ling, Cai, Yaqi, Yu, Zhaochen, Wang, Kai-Ni, Xie, Jiake, Tian, Ye, Xu, Minkai, Tang, Yong, Yang, Yujiu, Cui, Bin
Diffusion models have achieved remarkable advancements in text-to-image generation. However, existing models still have many difficulties when faced with multiple-object compositional generation. In this paper, we propose RealCompo, a new training-fr
Externí odkaz:
http://arxiv.org/abs/2402.12908
Autor:
Lyu, Cheng, Xie, Jiake, Xu, Bo, Lu, Cheng, Huang, Han, Huang, Xin, Wu, Ming, Zhang, Chuang, Tang, Yong
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. In this paper, w
Externí odkaz:
http://arxiv.org/abs/2211.14036
Generating semantic segmentation datasets has consistently been laborious and time-consuming, particularly in the context of large models or specialized domains(i.e. Medical Imaging or Remote Sensing). Specifically, large models necessitate a substan
Externí odkaz:
http://arxiv.org/abs/2211.11242
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 datasets make it difficult to fully explore the semantic association bet
Externí odkaz:
http://arxiv.org/abs/2204.09276
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 coarse-grained details. In general, these algorithms are incapable of f
Externí odkaz:
http://arxiv.org/abs/2106.14439
Autor:
Jia, Jiaru, Liu, Mingzhe, Xie, Jiake, Chen, Xin, Yang, Aiqing, Jiang, Xin, Zhang, Hong, Tang, Yong
Semantic segmentation models based on the conventional neural network can achieve remarkable performance in such tasks, while the dataset is crucial to the training model process. Significant progress in expanding datasets has been made in semi-super
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::206daa3f06bb328f2f698edfaadf7f02
http://arxiv.org/abs/2211.11242
http://arxiv.org/abs/2211.11242
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