Zobrazeno 1 - 10
of 374
pro vyhledávání: '"Luo, Mi"'
We study the problem of precisely swapping objects in videos, with a focus on those interacted with by hands, given one user-provided reference object image. Despite the great advancements that diffusion models have made in video editing recently, th
Externí odkaz:
http://arxiv.org/abs/2406.07754
We investigate exocentric-to-egocentric cross-view translation, which aims to generate a first-person (egocentric) view of an actor based on a video recording that captures the actor from a third-person (exocentric) perspective. To this end, we propo
Externí odkaz:
http://arxiv.org/abs/2403.06351
Autor:
Grauman, Kristen, Westbury, Andrew, Torresani, Lorenzo, Kitani, Kris, Malik, Jitendra, Afouras, Triantafyllos, Ashutosh, Kumar, Baiyya, Vijay, Bansal, Siddhant, Boote, Bikram, Byrne, Eugene, Chavis, Zach, Chen, Joya, Cheng, Feng, Chu, Fu-Jen, Crane, Sean, Dasgupta, Avijit, Dong, Jing, Escobar, Maria, Forigua, Cristhian, Gebreselasie, Abrham, Haresh, Sanjay, Huang, Jing, Islam, Md Mohaiminul, Jain, Suyog, Khirodkar, Rawal, Kukreja, Devansh, Liang, Kevin J, Liu, Jia-Wei, Majumder, Sagnik, Mao, Yongsen, Martin, Miguel, Mavroudi, Effrosyni, Nagarajan, Tushar, Ragusa, Francesco, Ramakrishnan, Santhosh Kumar, Seminara, Luigi, Somayazulu, Arjun, Song, Yale, Su, Shan, Xue, Zihui, Zhang, Edward, Zhang, Jinxu, Castillo, Angela, Chen, Changan, Fu, Xinzhu, Furuta, Ryosuke, Gonzalez, Cristina, Gupta, Prince, Hu, Jiabo, Huang, Yifei, Huang, Yiming, Khoo, Weslie, Kumar, Anush, Kuo, Robert, Lakhavani, Sach, Liu, Miao, Luo, Mi, Luo, Zhengyi, Meredith, Brighid, Miller, Austin, Oguntola, Oluwatumininu, Pan, Xiaqing, Peng, Penny, Pramanick, Shraman, Ramazanova, Merey, Ryan, Fiona, Shan, Wei, Somasundaram, Kiran, Song, Chenan, Southerland, Audrey, Tateno, Masatoshi, Wang, Huiyu, Wang, Yuchen, Yagi, Takuma, Yan, Mingfei, Yang, Xitong, Yu, Zecheng, Zha, Shengxin Cindy, Zhao, Chen, Zhao, Ziwei, Zhu, Zhifan, Zhuo, Jeff, Arbelaez, Pablo, Bertasius, Gedas, Crandall, David, Damen, Dima, Engel, Jakob, Farinella, Giovanni Maria, Furnari, Antonino, Ghanem, Bernard, Hoffman, Judy, Jawahar, C. V., Newcombe, Richard, Park, Hyun Soo, Rehg, James M., Sato, Yoichi, Savva, Manolis, Shi, Jianbo, Shou, Mike Zheng, Wray, Michael
We present Ego-Exo4D, a diverse, large-scale multimodal multiview video dataset and benchmark challenge. Ego-Exo4D centers around simultaneously-captured egocentric and exocentric video of skilled human activities (e.g., sports, music, dance, bike re
Externí odkaz:
http://arxiv.org/abs/2311.18259
Autor:
Yu, Weihao, Si, Chenyang, Zhou, Pan, Luo, Mi, Zhou, Yichen, Feng, Jiashi, Yan, Shuicheng, Wang, Xinchao
MetaFormer, the abstracted architecture of Transformer, has been found to play a significant role in achieving competitive performance. In this paper, we further explore the capacity of MetaFormer, again, without focusing on token mixer design: we in
Externí odkaz:
http://arxiv.org/abs/2210.13452
Publikováno v:
In Journal of Development Economics October 2024 171
Autor:
Anees, Shoaib Ahmad, Mehmood, Kaleem, Khan, Waseem Razzaq, Sajjad, Muhammad, Alahmadi, Tahani Awad, Alharbi, Sulaiman Ali, Luo, Mi
Publikováno v:
In Ecological Informatics September 2024 82
Autor:
Anees, Shoaib Ahmad, Mehmood, Kaleem, Rehman, Akhtar, Rehman, Nazir Ur, Muhammad, Sultan, Shahzad, Fahad, Hussain, Khadim, Luo, Mi, Alarfaj, Abdullah A., Alharbi, Sulaiman Ali, Khan, Waseem Razzaq
Publikováno v:
In Environmental and Sustainability Indicators December 2024 24
Autor:
Yu, Weihao, Luo, Mi, Zhou, Pan, Si, Chenyang, Zhou, Yichen, Wang, Xinchao, Feng, Jiashi, Yan, Shuicheng
Transformers have shown great potential in computer vision tasks. A common belief is their attention-based token mixer module contributes most to their competence. However, recent works show the attention-based module in Transformers can be replaced
Externí odkaz:
http://arxiv.org/abs/2111.11418
Autor:
Mehmood, Kaleem, Anees, Shoaib Ahmad, Luo, Mi, Akram, Muhammad, Zubair, Muhammad, Khan, Khalid Ali, Khan, Waseem Razzaq
Publikováno v:
In Trees, Forests and People June 2024 16
A central challenge in training classification models in the real-world federated system is learning with non-IID data. To cope with this, most of the existing works involve enforcing regularization in local optimization or improving the model aggreg
Externí odkaz:
http://arxiv.org/abs/2106.05001