no code implementations • 29 Nov 2022 • Taihong Xiao, ZiRui Wang, Liangliang Cao, Jiahui Yu, Shengyang Dai, Ming-Hsuan Yang
Vision-language foundation models pretrained on large-scale data provide a powerful tool for many visual understanding tasks.
1 code implementation • 4 Jul 2022 • Zhiwei Lin, TingTing Liang, Taihong Xiao, Yongtao Wang, Zhi Tang, Ming-Hsuan Yang
To address this issue, we propose a neural architecture search method named FlowNAS to automatically find the better encoder architecture for flow estimation task.
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 • 22 Sep 2021 • Taihong Xiao, Sifei Liu, Shalini De Mello, Zhiding Yu, Jan Kautz, Ming-Hsuan Yang
Dense correspondence across semantically related images has been extensively studied, but still faces two challenges: 1) large variations in appearance, scale and pose exist even for objects from the same category, and 2) labeling pixel-level dense correspondences is labor intensive and infeasible to scale.
1 code implementation • ECCV 2020 • Taihong Xiao, Jinwei Yuan, Deqing Sun, Qifei Wang, Xin-Yu Zhang, Kehan Xu, Ming-Hsuan Yang
Cost volume is an essential component of recent deep models for optical flow estimation and is usually constructed by calculating the inner product between two feature vectors.
1 code implementation • 8 Jul 2020 • Xin-Yu Zhang, Taihong Xiao, HaoLin Jia, Ming-Ming Cheng, Ming-Hsuan Yang
In this work, we propose a simple yet effective meta-learning algorithm in semi-supervised learning.
1 code implementation • 19 Feb 2020 • Xin-Yu Zhang, Kai Zhao, Taihong Xiao, Ming-Ming Cheng, Ming-Hsuan Yang
Recent advances in convolutional neural networks(CNNs) usually come with the expense of excessive computational overhead and memory footprint.
no code implementations • 22 Nov 2019 • Taihong Xiao, Yi-Hsuan Tsai, Kihyuk Sohn, Manmohan Chandraker, Ming-Hsuan Yang
For instance, there could be a potential privacy risk of machine learning systems via the model inversion attack, whose goal is to reconstruct the input data from the latent representation of deep networks.
2 code implementations • ECCV 2018 • Taihong Xiao, Jiapeng Hong, Jinwen Ma
Recent studies on face attribute transfer have achieved great success.
1 code implementation • ICLR 2018 • Taihong Xiao, Jiapeng Hong, Jinwen Ma
Disentangling factors of variation has become a very challenging problem on representation learning.
2 code implementations • 14 May 2017 • Shuchang Zhou, Taihong Xiao, Yi Yang, Dieqiao Feng, Qinyao He, Weiran He
In this work, we propose a model that can learn object transfiguration from two unpaired sets of images: one set containing images that "have" that kind of object, and the other set being the opposite, with the mild constraint that the objects be located approximately at the same place.