Zobrazeno 1 - 10
of 1 403
pro vyhledávání: '"Zhou Shijie"'
Diffusion models have emerged as a powerful foundation model for visual generation. With an appropriate sampling process, it can effectively serve as a generative prior to solve general inverse problems. Current posterior sampling based methods take
Externí odkaz:
http://arxiv.org/abs/2411.09850
Autor:
Fan, Zhiwen, Zhang, Jian, Cong, Wenyan, Wang, Peihao, Li, Renjie, Wen, Kairun, Zhou, Shijie, Kadambi, Achuta, Wang, Zhangyang, Xu, Danfei, Ivanovic, Boris, Pavone, Marco, Wang, Yue
Reconstructing and understanding 3D structures from a limited number of images is a well-established problem in computer vision. Traditional methods usually break this task into multiple subtasks, each requiring complex transformations between differ
Externí odkaz:
http://arxiv.org/abs/2410.18956
Autor:
Zhou, Shijie, Bradley, Jonathan R.
Wildfires have significantly increased in the United States (U.S.), making certain areas harder to live in. This motivates us to jointly analyze active fires and population changes in the U.S. from July 2020 to June 2021. The available data are recor
Externí odkaz:
http://arxiv.org/abs/2410.02905
Autor:
Li, Renjie, Pan, Panwang, Yang, Bangbang, Xu, Dejia, Zhou, Shijie, Zhang, Xuanyang, Li, Zeming, Kadambi, Achuta, Wang, Zhangyang, Tu, Zhengzhong, Fan, Zhiwen
The blooming of virtual reality and augmented reality (VR/AR) technologies has driven an increasing demand for the creation of high-quality, immersive, and dynamic environments. However, existing generative techniques either focus solely on dynamic o
Externí odkaz:
http://arxiv.org/abs/2406.13527
Investigating the network stability or synchronization dynamics of multi-agent systems with time delays is of significant importance in numerous real-world applications. Such investigations often rely on solving the transcendental characteristic equa
Externí odkaz:
http://arxiv.org/abs/2404.18704
Enhancing our understanding of adversarial examples is crucial for the secure application of machine learning models in real-world scenarios. A prevalent method for analyzing adversarial examples is through a frequency-based approach. However, existi
Externí odkaz:
http://arxiv.org/abs/2404.10202
Autor:
Zhou, Shijie, Fan, Zhiwen, Xu, Dejia, Chang, Haoran, Chari, Pradyumna, Bharadwaj, Tejas, You, Suya, Wang, Zhangyang, Kadambi, Achuta
The increasing demand for virtual reality applications has highlighted the significance of crafting immersive 3D assets. We present a text-to-3D 360$^{\circ}$ scene generation pipeline that facilitates the creation of comprehensive 360$^{\circ}$ scen
Externí odkaz:
http://arxiv.org/abs/2404.06903
Adversarial attacks in visual object tracking have significantly degraded the performance of advanced trackers by introducing imperceptible perturbations into images. However, there is still a lack of research on designing adversarial defense methods
Externí odkaz:
http://arxiv.org/abs/2402.17976
We introduce a novel machine unlearning framework founded upon the established principles of the min-max optimization paradigm. We capitalize on the capabilities of strong Membership Inference Attacks (MIA) to facilitate the unlearning of specific sa
Externí odkaz:
http://arxiv.org/abs/2402.06864
Autor:
Zhou, Shijie, Chang, Haoran, Jiang, Sicheng, Fan, Zhiwen, Zhu, Zehao, Xu, Dejia, Chari, Pradyumna, You, Suya, Wang, Zhangyang, Kadambi, Achuta
3D scene representations have gained immense popularity in recent years. Methods that use Neural Radiance fields are versatile for traditional tasks such as novel view synthesis. In recent times, some work has emerged that aims to extend the function
Externí odkaz:
http://arxiv.org/abs/2312.03203