Zobrazeno 1 - 10
of 137
pro vyhledávání: '"Pham‐Quoc, Cuong"'
3D object detection plays a crucial role in various applications such as autonomous vehicles, robotics and augmented reality. However, training 3D detectors requires a costly precise annotation, which is a hindrance to scaling annotation to large dat
Externí odkaz:
http://arxiv.org/abs/2407.17197
We introduce 3D-COCO, an extension of the original MS-COCO dataset providing 3D models and 2D-3D alignment annotations. 3D-COCO was designed to achieve computer vision tasks such as 3D reconstruction or image detection configurable with textual, 2D i
Externí odkaz:
http://arxiv.org/abs/2404.05641
Recent works have shown that objects discovery can largely benefit from the inherent motion information in video data. However, these methods lack a proper background processing, resulting in an over-segmentation of the non-object regions into random
Externí odkaz:
http://arxiv.org/abs/2311.02633
Autor:
Cioppa, Anthony, Giancola, Silvio, Somers, Vladimir, Magera, Floriane, Zhou, Xin, Mkhallati, Hassan, Deliège, Adrien, Held, Jan, Hinojosa, Carlos, Mansourian, Amir M., Miralles, Pierre, Barnich, Olivier, De Vleeschouwer, Christophe, Alahi, Alexandre, Ghanem, Bernard, Van Droogenbroeck, Marc, Kamal, Abdullah, Maglo, Adrien, Clapés, Albert, Abdelaziz, Amr, Xarles, Artur, Orcesi, Astrid, Scott, Atom, Liu, Bin, Lim, Byoungkwon, Chen, Chen, Deuser, Fabian, Yan, Feng, Yu, Fufu, Shitrit, Gal, Wang, Guanshuo, Choi, Gyusik, Kim, Hankyul, Guo, Hao, Fahrudin, Hasby, Koguchi, Hidenari, Ardö, Håkan, Salah, Ibrahim, Yerushalmy, Ido, Muhammad, Iftikar, Uchida, Ikuma, Be'ery, Ishay, Rabarisoa, Jaonary, Lee, Jeongae, Fu, Jiajun, Yin, Jianqin, Xu, Jinghang, Nang, Jongho, Denize, Julien, Li, Junjie, Zhang, Junpei, Kim, Juntae, Synowiec, Kamil, Kobayashi, Kenji, Zhang, Kexin, Habel, Konrad, Nakajima, Kota, Jiao, Licheng, Ma, Lin, Wang, Lizhi, Wang, Luping, Li, Menglong, Zhou, Mengying, Nasr, Mohamed, Abdelwahed, Mohamed, Liashuha, Mykola, Falaleev, Nikolay, Oswald, Norbert, Jia, Qiong, Pham, Quoc-Cuong, Song, Ran, Hérault, Romain, Peng, Rui, Chen, Ruilong, Liu, Ruixuan, Baikulov, Ruslan, Fukushima, Ryuto, Escalera, Sergio, Lee, Seungcheon, Chen, Shimin, Ding, Shouhong, Someya, Taiga, Moeslund, Thomas B., Li, Tianjiao, Shen, Wei, Zhang, Wei, Li, Wei, Dai, Wei, Luo, Weixin, Zhao, Wending, Zhang, Wenjie, Yang, Xinquan, Ma, Yanbiao, Joo, Yeeun, Zeng, Yingsen, Gan, Yiyang, Zhu, Yongqiang, Zhong, Yujie, Ruan, Zheng, Li, Zhiheng, Huang, Zhijian, Meng, Ziyu
The SoccerNet 2023 challenges were the third annual video understanding challenges organized by the SoccerNet team. For this third edition, the challenges were composed of seven vision-based tasks split into three main themes. The first theme, broadc
Externí odkaz:
http://arxiv.org/abs/2309.06006
Publikováno v:
Proceedings of The 2nd Conference on Lifelong Learning Agents, PMLR 232 (2023) 658-682
End-to-end reinforcement learning on images showed significant progress in the recent years. Data-based approach leverage data augmentation and domain randomization while representation learning methods use auxiliary losses to learn task-relevant fea
Externí odkaz:
http://arxiv.org/abs/2306.08537
Unsupervised object discovery aims to localize objects in images, while removing the dependence on annotations required by most deep learning-based methods. To address this problem, we propose a fully unsupervised, bottom-up approach, for multiple ob
Externí odkaz:
http://arxiv.org/abs/2212.10124
Autor:
Giancola, Silvio, Cioppa, Anthony, Deliège, Adrien, Magera, Floriane, Somers, Vladimir, Kang, Le, Zhou, Xin, Barnich, Olivier, De Vleeschouwer, Christophe, Alahi, Alexandre, Ghanem, Bernard, Van Droogenbroeck, Marc, Darwish, Abdulrahman, Maglo, Adrien, Clapés, Albert, Luyts, Andreas, Boiarov, Andrei, Xarles, Artur, Orcesi, Astrid, Shah, Avijit, Fan, Baoyu, Comandur, Bharath, Chen, Chen, Zhang, Chen, Zhao, Chen, Lin, Chengzhi, Chan, Cheuk-Yiu, Hui, Chun Chuen, Li, Dengjie, Yang, Fan, Liang, Fan, Da, Fang, Yan, Feng, Yu, Fufu, Wang, Guanshuo, Chan, H. Anthony, Zhu, He, Kan, Hongwei, Chu, Jiaming, Hu, Jianming, Gu, Jianyang, Chen, Jin, Soares, João V. B., Theiner, Jonas, De Corte, Jorge, Brito, José Henrique, Zhang, Jun, Li, Junjie, Liang, Junwei, Shen, Leqi, Ma, Lin, Chen, Lingchi, Marques, Miguel Santos, Azatov, Mike, Kasatkin, Nikita, Wang, Ning, Jia, Qiong, Pham, Quoc Cuong, Ewerth, Ralph, Song, Ran, Li, Rengang, Gade, Rikke, Debien, Ruben, Zhang, Runze, Lee, Sangrok, Escalera, Sergio, Jiang, Shan, Odashima, Shigeyuki, Chen, Shimin, Masui, Shoichi, Ding, Shouhong, Chan, Sin-wai, Chen, Siyu, El-Shabrawy, Tallal, He, Tao, Moeslund, Thomas B., Siu, Wan-Chi, Zhang, Wei, Li, Wei, Wang, Xiangwei, Tan, Xiao, Li, Xiaochuan, Wei, Xiaolin, Ye, Xiaoqing, Liu, Xing, Wang, Xinying, Guo, Yandong, Zhao, Yaqian, Yu, Yi, Li, Yingying, He, Yue, Zhong, Yujie, Guo, Zhenhua, Li, Zhiheng
The SoccerNet 2022 challenges were the second annual video understanding challenges organized by the SoccerNet team. In 2022, the challenges were composed of 6 vision-based tasks: (1) action spotting, focusing on retrieving action timestamps in long
Externí odkaz:
http://arxiv.org/abs/2210.02365
Tracking the players and the ball in team sports is key to analyse the performance or to enhance the game watching experience with augmented reality. When the only sources for this data are broadcast videos, sports-field registration systems are requ
Externí odkaz:
http://arxiv.org/abs/2209.07795
We present a new self-supervised pre-training of Vision Transformers for dense prediction tasks. It is based on a contrastive loss across views that compares pixel-level representations to global image representations. This strategy produces better l
Externí odkaz:
http://arxiv.org/abs/2205.15173
One of the requirements for team sports analysis is to track and recognize players. Many tracking and reidentification methods have been proposed in the context of video surveillance. They show very convincing results when tested on public datasets s
Externí odkaz:
http://arxiv.org/abs/2204.04049