Zobrazeno 1 - 10
of 48
pro vyhledávání: '"Xiao, Zihao"'
Face Forgery videos have elicited critical social public concerns and various detectors have been proposed. However, fully-supervised detectors may lead to easily overfitting to specific forgery methods or videos, and existing self-supervised detecto
Externí odkaz:
http://arxiv.org/abs/2407.10550
Autor:
Cai, Yuanhao, Xiao, Zihao, Liang, Yixun, Qin, Minghan, Zhang, Yulun, Yang, Xiaokang, Liu, Yaoyao, Yuille, Alan
High dynamic range (HDR) novel view synthesis (NVS) aims to create photorealistic images from novel viewpoints using HDR imaging techniques. The rendered HDR images capture a wider range of brightness levels containing more details of the scene than
Externí odkaz:
http://arxiv.org/abs/2405.15125
Autor:
Xiao, Zihao, Jing, Longlong, Wu, Shangxuan, Zhu, Alex Zihao, Ji, Jingwei, Jiang, Chiyu Max, Hung, Wei-Chih, Funkhouser, Thomas, Kuo, Weicheng, Angelova, Anelia, Zhou, Yin, Sheng, Shiwei
3D panoptic segmentation is a challenging perception task, especially in autonomous driving. It aims to predict both semantic and instance annotations for 3D points in a scene. Although prior 3D panoptic segmentation approaches have achieved great pe
Externí odkaz:
http://arxiv.org/abs/2401.02402
Face forgery videos have caused severe public concerns, and many detectors have been proposed. However, most of these detectors suffer from limited generalization when detecting videos from unknown distributions, such as from unseen forgery methods.
Externí odkaz:
http://arxiv.org/abs/2309.04795
Autor:
Ma, Wufei, Liu, Qihao, Wang, Jiahao, Wang, Angtian, Yuan, Xiaoding, Zhang, Yi, Xiao, Zihao, Zhang, Guofeng, Lu, Beijia, Duan, Ruxiao, Qi, Yongrui, Kortylewski, Adam, Liu, Yaoyao, Yuille, Alan
Diffusion models have emerged as a powerful generative method, capable of producing stunning photo-realistic images from natural language descriptions. However, these models lack explicit control over the 3D structure in the generated images. Consequ
Externí odkaz:
http://arxiv.org/abs/2306.08103
Autor:
Li, Jianhui, Li, Jianmin, Zhang, Haoji, Liu, Shilong, Wang, Zhengyi, Xiao, Zihao, Zheng, Kaiwen, Zhu, Jun
We study the 3D-aware image attribute editing problem in this paper, which has wide applications in practice. Recent methods solved the problem by training a shared encoder to map images into a 3D generator's latent space or by per-image latent code
Externí odkaz:
http://arxiv.org/abs/2304.10263
Deep neural networks (DNNs) have been shown to be vulnerable to adversarial examples. Moreover, the transferability of the adversarial examples has received broad attention in recent years, which means that adversarial examples crafted by a surrogate
Externí odkaz:
http://arxiv.org/abs/2304.06908
In this work, we tackle two vital tasks in automated driving systems, i.e., driver intent prediction and risk object identification from egocentric images. Mainly, we investigate the question: what would be good road scene-level representations for t
Externí odkaz:
http://arxiv.org/abs/2301.00714
Deep neural networks (DNNs) are vulnerable to adversarial examples. And, the adversarial examples have transferability, which means that an adversarial example for a DNN model can fool another model with a non-trivial probability. This gave birth to
Externí odkaz:
http://arxiv.org/abs/2206.08316
Recent studies have revealed the vulnerability of face recognition models against physical adversarial patches, which raises security concerns about the deployed face recognition systems. However, it is still challenging to ensure the reproducibility
Externí odkaz:
http://arxiv.org/abs/2203.04623