SoccerNet 2024 Challenges Results

Autor: Cioppa, Anthony, Giancola, Silvio, Somers, Vladimir, Joos, Victor, Magera, Floriane, Held, Jan, Ghasemzadeh, Seyed Abolfazl, Zhou, Xin, Seweryn, Karolina, Kowalczyk, Mateusz, Mróz, Zuzanna, Łukasik, Szymon, Hałoń, Michał, Mkhallati, Hassan, Deliège, Adrien, Hinojosa, Carlos, Sanchez, Karen, Mansourian, Amir M., Miralles, Pierre, Barnich, Olivier, De Vleeschouwer, Christophe, Alahi, Alexandre, Ghanem, Bernard, Van Droogenbroeck, Marc, Gorski, Adam, Clapés, Albert, Boiarov, Andrei, Afanasiev, Anton, Xarles, Artur, Scott, Atom, Lim, ByoungKwon, Yeung, Calvin, Gonzalez, Cristian, Rüfenacht, Dominic, Pacilio, Enzo, Deuser, Fabian, Altawijri, Faisal Sami, Cachón, Francisco, Kim, HanKyul, Wang, Haobo, Choe, Hyeonmin, Kim, Hyunwoo J, Kim, Il-Min, Kang, Jae-Mo, Tursunboev, Jamshid, Yang, Jian, Hong, Jihwan, Lee, Jimin, Zhang, Jing, Lee, Junseok, Zhang, Kexin, Habel, Konrad, Jiao, Licheng, Li, Linyi, Gutiérrez-Pérez, Marc, Ortega, Marcelo, Li, Menglong, Lopatto, Milosz, Kasatkin, Nikita, Nemtsev, Nikolay, Oswald, Norbert, Udin, Oleg, Kononov, Pavel, Geng, Pei, Alotaibi, Saad Ghazai, Kim, Sehyung, Ulasen, Sergei, Escalera, Sergio, Zhang, Shanshan, Yang, Shuyuan, Moon, Sunghwan, Moeslund, Thomas B., Shandyba, Vasyl, Golovkin, Vladimir, Dai, Wei, Chung, WonTaek, Liu, Xinyu, Zhu, Yongqiang, Kim, Youngseo, Li, Yuan, Yang, Yuting, Xiao, Yuxuan, Cheng, Zehua, Li, Zhihao

The SoccerNet 2024 challenges represent the fourth annual video understanding challenges organized by the SoccerNet team. These challenges aim to advance research across multiple themes in football, including broadcast video understanding, field unde

Externí odkaz: http://arxiv.org/abs/2409.10587

An Empirical Study into Clustering of Unseen Datasets with Self-Supervised Encoders

Autor: Lowe, Scott C., Haurum, Joakim Bruslund, Oore, Sageev, Moeslund, Thomas B., Taylor, Graham W.

Can pretrained models generalize to new datasets without any retraining? We deploy pretrained image models on datasets they were not trained for, and investigate whether their embeddings form meaningful clusters. Our suite of benchmarking experiments

Externí odkaz: http://arxiv.org/abs/2406.02465

AI Competitions and Benchmarks: Dataset Development

Autor: Egele, Romain, Junior, Julio C. S. Jacques, van Rijn, Jan N., Guyon, Isabelle, Baró, Xavier, Clapés, Albert, Balaprakash, Prasanna, Escalera, Sergio, Moeslund, Thomas, Wan, Jun

Machine learning is now used in many applications thanks to its ability to predict, generate, or discover patterns from large quantities of data. However, the process of collecting and transforming data for practical use is intricate. Even in today's

Externí odkaz: http://arxiv.org/abs/2404.09703

T-DEED: Temporal-Discriminability Enhancer Encoder-Decoder for Precise Event Spotting in Sports Videos

Autor: Xarles, Artur, Escalera, Sergio, Moeslund, Thomas B., Clapés, Albert

In this paper, we introduce T-DEED, a Temporal-Discriminability Enhancer Encoder-Decoder for Precise Event Spotting in sports videos. T-DEED addresses multiple challenges in the task, including the need for discriminability among frame representation

Externí odkaz: http://arxiv.org/abs/2404.05392

A noisy elephant in the room: Is your out-of-distribution detector robust to label noise?

Autor: Humblot-Renaux, Galadrielle, Escalera, Sergio, Moeslund, Thomas B.

The ability to detect unfamiliar or unexpected images is essential for safe deployment of computer vision systems. In the context of classification, the task of detecting images outside of a model's training domain is known as out-of-distribution (OO

Externí odkaz: http://arxiv.org/abs/2404.01775