1 code implementation • 24 May 2024 • Yuanhao Cai, Zihao Xiao, Yixun Liang, Minghan Qin, Yulun Zhang, Xiaokang Yang, Yaoyao Liu, Alan Yuille
In this paper, we propose a new framework, High Dynamic Range Gaussian Splatting (HDR-GS), which can efficiently render novel HDR views and reconstruct LDR images with a user input exposure time.
no code implementations • 4 Jan 2024 • Zihao Xiao, Longlong Jing, Shangxuan Wu, Alex Zihao Zhu, Jingwei Ji, Chiyu Max Jiang, Wei-Chih Hung, Thomas Funkhouser, Weicheng Kuo, Anelia Angelova, Yin Zhou, Shiwei Sheng
3D panoptic segmentation is a challenging perception task, especially in autonomous driving.
no code implementations • 30 Nov 2023 • Jiawei Peng, Ju He, Prakhar Kaushik, Zihao Xiao, Jiteng Mu, Alan Yuille
We then benchmark Syn-to-Real animal part segmentation from SAP to PartImageNet, namely SynRealPart, with existing semantic segmentation domain adaptation methods and further improve them as our second contribution.
no code implementations • 9 Sep 2023 • Daichi Zhang, Zihao Xiao, Jianmin Li, Shiming Ge
In this paper, a Self-supervised Transformer cooperating with Contrastive and Reconstruction learning (CoReST) is proposed, which is first pre-trained only on real face videos in a self-supervised manner, and then fine-tuned a linear head on specific face forgery video datasets.
no code implementations • 13 Jun 2023 • Wufei Ma, Qihao Liu, Jiahao Wang, Angtian Wang, Xiaoding Yuan, Yi Zhang, Zihao Xiao, Guofeng Zhang, Beijia Lu, Ruxiao Duan, Yongrui Qi, Adam Kortylewski, Yaoyao Liu, Alan Yuille
With explicit 3D geometry control, we can easily change the 3D structures of the objects in the generated images and obtain ground-truth 3D annotations automatically.
1 code implementation • CVPR 2023 • Jianhui Li, Jianmin Li, Haoji Zhang, Shilong Liu, Zhengyi Wang, Zihao Xiao, Kaiwen Zheng, Jun Zhu
As for imprecise image editing, we attribute the problem to the gap between the latent space of real images and that of generated images.
2 code implementations • 14 Apr 2023 • Dingcheng Yang, Wenjian Yu, Zihao Xiao, Jiaqi Luo
In this paper, we propose to improve the transferability of adversarial examples in the transfer-based attack via masking unimportant parameters (MUP).
no code implementations • 2 Jan 2023 • Zihao Xiao, Alan Yuille, Yi-Ting Chen
In this work, we tackle two vital tasks in automated driving systems, i. e., driver intent prediction and risk object identification from egocentric images.
2 code implementations • 16 Jun 2022 • Dingcheng Yang, Zihao Xiao, Wenjian Yu
This paper proposes a method for training a surrogate model with dark knowledge to boost the transferability of the adversarial examples generated by the surrogate model.
no code implementations • 9 Mar 2022 • Xiao Yang, Yinpeng Dong, Tianyu Pang, Zihao Xiao, Hang Su, Jun Zhu
It is therefore imperative to develop a framework that can enable a comprehensive evaluation of the vulnerability of face recognition in the physical world.
no code implementations • 14 Oct 2021 • Xinyue Wei, Weichao Qiu, Yi Zhang, Zihao Xiao, Alan Yuille
Nuisance factors are those irrelevant to a task, and an ideal model should be invariant to them.
no code implementations • CVPR 2021 • Zihao Xiao, Xianfeng Gao, Chilin Fu, Yinpeng Dong, Wei Gao, Xiaolu Zhang, Jun Zhou, Jun Zhu
However, deep CNNs are vulnerable to adversarial patches, which are physically realizable and stealthy, raising new security concerns on the real-world applications of these models.
no code implementations • ICCV 2021 • Yinpeng Dong, Xiao Yang, Zhijie Deng, Tianyu Pang, Zihao Xiao, Hang Su, Jun Zhu
Although deep neural networks (DNNs) have made rapid progress in recent years, they are vulnerable in adversarial environments.
no code implementations • 26 Dec 2019 • Yinpeng Dong, Qi-An Fu, Xiao Yang, Tianyu Pang, Hang Su, Zihao Xiao, Jun Zhu
Deep neural networks are vulnerable to adversarial examples, which becomes one of the most important research problems in the development of deep learning.
no code implementations • 8 Dec 2019 • Tae Soo Kim, Jonathan D. Jones, Michael Peven, Zihao Xiao, Jin Bai, Yi Zhang, Weichao Qiu, Alan Yuille, Gregory D. Hager
There are many realistic applications of activity recognition where the set of potential activity descriptions is combinatorially large.
no code implementations • 3 Dec 2019 • Yi Zhang, Xinyue Wei, Weichao Qiu, Zihao Xiao, Gregory D. Hager, Alan Yuille
In this paper, we propose the Randomized Simulation as Augmentation (RSA) framework which augments real-world training data with synthetic data to improve the robustness of action recognition networks.
no code implementations • 10 Apr 2018 • Zihao Xiao, Jianfei Chen, Jun Zhu
We also propose an extension to train pLSI and a method to prune the network to obey the limited fan-in of some NMSs.