Zobrazeno 1 - 10
of 18
pro vyhledávání: '"Peruzzo, Elia"'
Autor:
D'Incà, Moreno, Peruzzo, Elia, Mancini, Massimiliano, Xu, Xingqian, Shi, Humphrey, Sebe, Nicu
Recent progress in Text-to-Image (T2I) generative models has enabled high-quality image generation. As performance and accessibility increase, these models are gaining significant attraction and popularity: ensuring their fairness and safety is a pri
Externí odkaz:
http://arxiv.org/abs/2408.16700
Autor:
D'Incà, Moreno, Peruzzo, Elia, Mancini, Massimiliano, Xu, Dejia, Goel, Vidit, Xu, Xingqian, Wang, Zhangyang, Shi, Humphrey, Sebe, Nicu
Text-to-image generative models are becoming increasingly popular and accessible to the general public. As these models see large-scale deployments, it is necessary to deeply investigate their safety and fairness to not disseminate and perpetuate any
Externí odkaz:
http://arxiv.org/abs/2404.07990
Autor:
Peruzzo, Elia, Goel, Vidit, Xu, Dejia, Xu, Xingqian, Jiang, Yifan, Wang, Zhangyang, Shi, Humphrey, Sebe, Nicu
Recently, several works tackled the video editing task fostered by the success of large-scale text-to-image generative models. However, most of these methods holistically edit the frame using the text, exploiting the prior given by foundation diffusi
Externí odkaz:
http://arxiv.org/abs/2401.02473
Autor:
Dall'Asen, Nicola, Menapace, Willi, Peruzzo, Elia, Sangineto, Enver, Wang, Yiming, Ricci, Elisa
The process of painting fosters creativity and rational planning. However, existing generative AI mostly focuses on producing visually pleasant artworks, without emphasizing the painting process. We introduce a novel task, Collaborative Neural Painti
Externí odkaz:
http://arxiv.org/abs/2312.01800
Autor:
Peruzzo, Elia, Menapace, Willi, Goel, Vidit, Arrigoni, Federica, Tang, Hao, Xu, Xingqian, Chopikyan, Arman, Orlov, Nikita, Hu, Yuxiao, Shi, Humphrey, Sebe, Nicu, Ricci, Elisa
In the last few years, Neural Painting (NP) techniques became capable of producing extremely realistic artworks. This paper advances the state of the art in this emerging research domain by proposing the first approach for Interactive NP. Considering
Externí odkaz:
http://arxiv.org/abs/2307.16441
Autor:
Goel, Vidit, Peruzzo, Elia, Jiang, Yifan, Xu, Dejia, Xu, Xingqian, Sebe, Nicu, Darrell, Trevor, Wang, Zhangyang, Shi, Humphrey
Generative image editing has recently witnessed extremely fast-paced growth. Some works use high-level conditioning such as text, while others use low-level conditioning. Nevertheless, most of them lack fine-grained control over the properties of the
Externí odkaz:
http://arxiv.org/abs/2303.17546
Autor:
Peruzzo, Elia, Sangineto, Enver, Liu, Yahui, De Nadai, Marco, Bi, Wei, Lepri, Bruno, Sebe, Nicu
Recent work on Vision Transformers (VTs) showed that introducing a local inductive bias in the VT architecture helps reducing the number of samples necessary for training. However, the architecture modifications lead to a loss of generality of the Tr
Externí odkaz:
http://arxiv.org/abs/2206.04636
Akademický článek
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Autor:
Goel, Vidit, Peruzzo, Elia, Jiang, Yifan, Xu, Dejia, Sebe, Nicu, Darrell, Trevor, Wang, Zhangyang, Shi, Humphrey
Image editing using diffusion models has witnessed extremely fast-paced growth recently. There are various ways in which previous works enable controlling and editing images. Some works use high-level conditioning such as text, while others use low-l
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::bc1b446d5f3e3b2d76d6b1f81ef58f63
Akademický článek
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