SoccerNet 2023 Challenges Results

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

VIBR: Learning View-Invariant Value Functions for Robust Visual Control

Autor: Dupuis, Tom, Rabarisoa, Jaonary, Pham, Quoc-Cuong, Filliat, David

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

Self-Supervised Pre-training of Vision Transformers for Dense Prediction Tasks

Autor: Rabarisoa, Jaonary, Belissen, Valentin, Chabot, Florian, Pham, Quoc-Cuong

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

Similarity Contrastive Estimation for Self-Supervised Soft Contrastive Learning

Autor: Denize, Julien, Rabarisoa, Jaonary, Orcesi, Astrid, Hérault, Romain, Canu, Stéphane

Publikováno v: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) 2023

Contrastive representation learning has proven to be an effective self-supervised learning method. Most successful approaches are based on Noise Contrastive Estimation (NCE) and use different views of an instance as positives that should be contraste

Externí odkaz: http://arxiv.org/abs/2111.14585

Similarity contrastive estimation for image and video soft contrastive self-supervised learning.

Autor: Denize, Julien^1,2 (AUTHOR) julien.denize@cea.fr, Rabarisoa, Jaonary¹ (AUTHOR), Orcesi, Astrid¹ (AUTHOR), Hérault, Romain² (AUTHOR)

Publikováno v: Machine Vision & Applications. Nov2023, Vol. 34 Issue 6, p1-19. 19p.