Zobrazeno 1 - 10
of 111
pro vyhledávání: '"Hérault, Romain"'
Adversarial learning baselines for domain adaptation (DA) approaches in the context of semantic segmentation are under explored in semi-supervised framework. These baselines involve solely the available labeled target samples in the supervision loss.
Externí odkaz:
http://arxiv.org/abs/2312.07370
Domain-to-domain translation involves generating a target domain sample given a condition in the source domain. Most existing methods focus on fixed input and output domains, i.e. they only work for specific configurations (i.e. for two domains, eith
Externí odkaz:
http://arxiv.org/abs/2309.14394
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
COMEDIAN: Self-Supervised Learning and Knowledge Distillation for Action Spotting using Transformers
We present COMEDIAN, a novel pipeline to initialize spatiotemporal transformers for action spotting, which involves self-supervised learning and knowledge distillation. Action spotting is a timestamp-level temporal action detection task. Our pipeline
Externí odkaz:
http://arxiv.org/abs/2309.01270
Contrastive representation learning has proven to be an effective self-supervised learning method for images and videos. Most successful approaches are based on Noise Contrastive Estimation (NCE) and use different views of an instance as positives th
Externí odkaz:
http://arxiv.org/abs/2212.11187
In this paper, we investigate the problem of multi-domain translation: given an element $a$ of domain $A$, we would like to generate a corresponding $b$ sample in another domain $B$, and vice versa. Acquiring supervision in multiple domains can be a
Externí odkaz:
http://arxiv.org/abs/2212.03361
Autor:
Ruffino, Cyprien, Blin, Rachel, Ainouz, Samia, Gasso, Gilles, Hérault, Romain, Meriaudeau, Fabrice, Canu, Stéphane
Polarimetric imaging, along with deep learning, has shown improved performances on different tasks including scene analysis. However, its robustness may be questioned because of the small size of the training datasets. Though the issue could be solve
Externí odkaz:
http://arxiv.org/abs/2206.07431
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
We present a 2-step optimal transport approach that performs a mapping from a source distribution to a target distribution. Here, the target has the particularity to present new classes not present in the source domain. The first step of the approach
Externí odkaz:
http://arxiv.org/abs/2010.01045
Generative Adversarial Networks (GANs) have proven successful for unsupervised image generation. Several works have extended GANs to image inpainting by conditioning the generation with parts of the image to be reconstructed. Despite their success, t
Externí odkaz:
http://arxiv.org/abs/2002.01281