no code implementations • 26 Jul 2023 • Hong Lu, Chuan Li, Yinheng Li, Jie Zhao
The drug development process necessitates that pharmacologists undertake various tasks, such as reviewing literature, formulating hypotheses, designing experiments, and interpreting results.
no code implementations • 2 Nov 2022 • Minjie Lei, Ka Vang Tsang, Sean Gasiorowski, Chuan Li, Youssef Nashed, Gianluca Petrillo, Olivia Piazza, Daniel Ratner, Kazuhiro Terao
As the size of a table increases with detector volume for a fixed resolution, this method scales poorly for future larger detectors.
2 code implementations • 5 Oct 2022 • Justin N. M. Pinkney, Chuan Li
We introduce a new method to efficiently create text-to-image models from a pre-trained CLIP and StyleGAN.
no code implementations • ICCV 2021 • Yassir Saquil, Da Chen, Yuan He, Chuan Li, Yong-Liang Yang
In this paper, we investigate video summarization in the supervised setting.
no code implementations • 1 Sep 2020 • Paul L. Rosin, Yu-Kun Lai, David Mould, Ran Yi, Itamar Berger, Lars Doyle, Seungyong Lee, Chuan Li, Yong-Jin Liu, Amir Semmo, Ariel Shamir, Minjung Son, Holger Winnemoller
Despite the recent upsurge of activity in image-based non-photorealistic rendering (NPR), and in particular portrait image stylisation, due to the advent of neural style transfer, the state of performance evaluation in this field is limited, especially compared to the norms in the computer vision and machine learning communities.
3 code implementations • ICCV 2019 • Thu Nguyen-Phuoc, Chuan Li, Lucas Theis, Christian Richardt, Yong-Liang Yang
This shows that HoloGAN is the first generative model that learns 3D representations from natural images in an entirely unsupervised manner.
1 code implementation • NeurIPS 2018 • Thu Nguyen-Phuoc, Chuan Li, Stephen Balaban, Yong-Liang Yang
We present RenderNet, a differentiable rendering convolutional network with a novel projection unit that can render 2D images from 3D shapes.
2 code implementations • 15 Apr 2016 • Chuan Li, Michael Wand
This paper proposes Markovian Generative Adversarial Networks (MGANs), a method for training generative neural networks for efficient texture synthesis.
7 code implementations • CVPR 2016 • Chuan Li, Michael Wand
This paper studies a combination of generative Markov random field (MRF) models and discriminatively trained deep convolutional neural networks (dCNNs) for synthesizing 2D images.