Zobrazeno 1 - 10
of 399
pro vyhledávání: '"Jawahar C"'
Autor:
Khindkar, Vaishnavi, Balasubramanian, Vineeth, Arora, Chetan, Subramanian, Anbumani, Jawahar, C. V.
With the increased importance of autonomous navigation systems has come an increasing need to protect the safety of Vulnerable Road Users (VRUs) such as pedestrians. Predicting pedestrian intent is one such challenging task, where prior work predicts
Externí odkaz:
http://arxiv.org/abs/2411.13302
Question-answering handwritten documents is a challenging task with numerous real-world applications. This paper proposes a novel recognition-based approach that improves upon the previous state-of-the-art on the HW-SQuAD and BenthamQA datasets. Our
Externí odkaz:
http://arxiv.org/abs/2406.17437
Intelligent vehicle systems require a deep understanding of the interplay between road conditions, surrounding entities, and the ego vehicle's driving behavior for safe and efficient navigation. This is particularly critical in developing countries w
Externí odkaz:
http://arxiv.org/abs/2404.08561
Publikováno v:
ICDAR 2023 Workshops. Lecture Notes in Computer Science, vol 14193. Springer, Cham (2023)
The importance of Scene Text Recognition (STR) in today's increasingly digital world cannot be overstated. Given the significance of STR, data intensive deep learning approaches that auto-learn feature mappings have primarily driven the development o
Externí odkaz:
http://arxiv.org/abs/2403.08007
Publikováno v:
In Proceedings of the 31st ACM International Conference on Multimedia, 2023
In this paper, we introduce a novel approach to address the task of synthesizing speech from silent videos of any in-the-wild speaker solely based on lip movements. The traditional approach of directly generating speech from lip videos faces the chal
Externí odkaz:
http://arxiv.org/abs/2403.01087
The effective management of brain tumors relies on precise typing, subtyping, and grading. This study advances patient care with findings from rigorous multiple instance learning experimentations across various feature extractors and aggregators in b
Externí odkaz:
http://arxiv.org/abs/2402.15832
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
Given multiple videos of the same task, procedure learning addresses identifying the key-steps and determining their order to perform the task. For this purpose, existing approaches use the signal generated from a pair of videos. This makes key-steps
Externí odkaz:
http://arxiv.org/abs/2311.03550