no code implementations • 22 Mar 2024 • Kevin Xie, Jonathan Lorraine, Tianshi Cao, Jun Gao, James Lucas, Antonio Torralba, Sanja Fidler, Xiaohui Zeng
Recent text-to-3D generation approaches produce impressive 3D results but require time-consuming optimization that can take up to an hour per prompt.
no code implementations • ICCV 2023 • Tianshi Cao, Karsten Kreis, Sanja Fidler, Nicholas Sharp, Kangxue Yin
We present TexFusion (Texture Diffusion), a new method to synthesize textures for given 3D geometries, using large-scale text-guided image diffusion models.
1 code implementation • 18 Oct 2022 • Tim Dockhorn, Tianshi Cao, Arash Vahdat, Karsten Kreis
While modern machine learning models rely on increasingly large training datasets, data is often limited in privacy-sensitive domains.
no code implementations • NeurIPS 2021 • Tianshi Cao, Sasha Doubov, David Acuna, Sanja Fidler
NDS uses a mixture of experts trained on data sources to estimate similarity between each source and the downstream task.
no code implementations • NeurIPS 2021 • Tianshi Cao, Alex Bie, Arash Vahdat, Sanja Fidler, Karsten Kreis
Generative models trained with privacy constraints on private data can sidestep this challenge, providing indirect access to private data instead.
1 code implementation • 1 Nov 2021 • Tianshi Cao, Alex Bie, Arash Vahdat, Sanja Fidler, Karsten Kreis
Generative models trained with privacy constraints on private data can sidestep this challenge, providing indirect access to private data instead.
no code implementations • 1 Jan 2021 • Tianshi Cao, Alex Bie, Karsten Kreis, Sanja Fidler
Generative models trained with privacy constraints on private data can sidestep this challenge and provide indirect access to the private data instead.
3 code implementations • 8 Jul 2020 • Tianshi Cao, Chin-wei Huang, David Yu-Tung Hui, Joseph Paul Cohen
However it is unclear which OoDD method should be used in practice.
1 code implementation • 15 Apr 2020 • Tianshi Cao, Jingkang Wang, Yining Zhang, Sivabalan Manivasagam
To facilitate the research on language-guided agents with domain adaption, we propose a novel zero-shot compositional policy learning task, where the environments are characterized as a composition of different attributes.
no code implementations • MIDL 2019 • Tianshi Cao, David Yu-Tung Hui, Chinwei Huang, Joseph Paul Cohen
There is a rise in the use of deep learning for automated medical diagnosis, most notably in medical imaging.
no code implementations • ICLR 2020 • Tianshi Cao, Marc Law, Sanja Fidler
We introduce a theoretical analysis of the impact of the shot number on Prototypical Networks, a state-of-the-art few-shot classification method.