no code implementations • 1 Dec 2023 • Litu Rout, Yujia Chen, Abhishek Kumar, Constantine Caramanis, Sanjay Shakkottai, Wen-Sheng Chu
To our best knowledge, this is the first work to offer an efficient second-order approximation in solving inverse problems using latent diffusion and editing real-world images with corruptions.
1 code implementation • 16 Sep 2023 • Jiaheng Wei, Harikrishna Narasimhan, Ehsan Amid, Wen-Sheng Chu, Yang Liu, Abhishek Kumar
We investigate the problem of training models that are robust to shifts caused by changes in the distribution of class-priors or group-priors.
1 code implementation • CVPR 2023 • Yiyou Sun, Yaojie Liu, Xiaoming Liu, Yixuan Li, Wen-Sheng Chu
This work studies the generalization issue of face anti-spoofing (FAS) models on domain gaps, such as image resolution, blurriness and sensor variations.
no code implementations • 23 Mar 2022 • Hsin-Ping Huang, Deqing Sun, Yaojie Liu, Wen-Sheng Chu, Taihong Xiao, Jinwei Yuan, Hartwig Adam, Ming-Hsuan Yang
While recent face anti-spoofing methods perform well under the intra-domain setups, an effective approach needs to account for much larger appearance variations of images acquired in complex scenes with different sensors for robust performance.
no code implementations • 16 Dec 2021 • Giannis Daras, Wen-Sheng Chu, Abhishek Kumar, Dmitry Lagun, Alexandros G. Dimakis
We introduce a novel framework for solving inverse problems using NeRF-style generative models.
1 code implementation • ICCV 2021 • Min Jin Chong, Wen-Sheng Chu, Abhishek Kumar, David Forsyth
We present Retrieve in Style (RIS), an unsupervised framework for facial feature transfer and retrieval on real images.
no code implementations • 22 Mar 2021 • Shlok Kumar Mishra, Kuntal Sengupta, Max Horowitz-Gelb, Wen-Sheng Chu, Sofien Bouaziz, David Jacobs
Presentation attack detection (PAD) is a critical component in secure face authentication.
1 code implementation • 22 Oct 2020 • Esther Robb, Wen-Sheng Chu, Abhishek Kumar, Jia-Bin Huang
We validate our method in a challenging few-shot setting of 5-100 images in the target domain.
no code implementations • CVPR 2018 • Kaili Zhao, Wen-Sheng Chu, Aleix M. Martinez
We present a scalable weakly supervised clustering approach to learn facial action units (AUs) from large, freely available web images.
no code implementations • 2 Aug 2016 • Wen-Sheng Chu, Fernando de la Torre, Jeffrey F. Cohn
To model temporal dependencies, Long Short-Term Memory (LSTMs) are stacked on top of these representations, regardless of the lengths of input videos.
2 code implementations • CVPR 2016 • Kaili Zhao, Wen-Sheng Chu, Honggang Zhang
Region learning (RL) and multi-label learning (ML) have recently attracted increasing attentions in the field of facial Action Unit (AU) detection.
Ranked #6 on Facial Action Unit Detection on DISFA
no code implementations • 25 Mar 2016 • Zhuo Hui, Wen-Sheng Chu
We compared linear (PCA and KPCA), manifold (LPP and LLE), supervised (LDA and KDA) and hybrid approaches (LSDA) to DR with respect to AU detection.
no code implementations • ICCV 2015 • Jiabei Zeng, Wen-Sheng Chu, Fernando de la Torre, Jeffrey F. Cohn, Zhang Xiong
Varied sources of error contribute to the challenge of facial action unit detection.
no code implementations • ICCV 2015 • Wen-Sheng Chu, Jiabei Zeng, Fernando de la Torre, Jeffrey F. Cohn, Daniel S. Messinger
We evaluate the effectiveness of our approach in multiple databases, including human actions using the CMU Mocap dataset, spontaneous facial behaviors using group-formation task dataset and parent-infant interaction dataset.
no code implementations • CVPR 2015 • Wen-Sheng Chu, Yale Song, Alejandro Jaimes
We present video co-summarization, a novel perspective to video summarization that exploits visual co-occurrence across multiple videos.
no code implementations • CVPR 2015 • Kaili Zhao, Wen-Sheng Chu, Fernando de la Torre, Jeffrey F. Cohn, Honggang Zhang
The most commonly used taxonomy to describe facial behaviour is the Facial Action Coding System (FACS).
no code implementations • CVPR 2013 • Wen-Sheng Chu, Fernando de la Torre, Jeffery F. Cohn
To evaluate the effectiveness of STM, we compared STM to generic classifiers and to cross-domain learning methods in three major databases: CK+ [20], GEMEP-FERA [32] and RU-FACS [2].