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pro vyhledávání: '"Hannan, Tanveer"'
Remote sensing projects typically generate large amounts of imagery that can be used to train powerful deep neural networks. However, the amount of labeled images is often small, as remote sensing applications generally require expert labelers. Thus,
Externí odkaz:
http://arxiv.org/abs/2404.18583
Locating specific moments within long videos (20-120 minutes) presents a significant challenge, akin to finding a needle in a haystack. Adapting existing short video (5-30 seconds) grounding methods to this problem yields poor performance. Since most
Externí odkaz:
http://arxiv.org/abs/2312.06729
Autor:
Hannan, Tanveer, Koner, Rajat, Bernhard, Maximilian, Shit, Suprosanna, Menze, Bjoern, Tresp, Volker, Schubert, Matthias, Seidl, Thomas
Recent trends in Video Instance Segmentation (VIS) have seen a growing reliance on online methods to model complex and lengthy video sequences. However, the degradation of representation and noise accumulation of the online methods, especially during
Externí odkaz:
http://arxiv.org/abs/2305.17096
Autor:
Koner, Rajat, Hannan, Tanveer, Shit, Suprosanna, Sharifzadeh, Sahand, Schubert, Matthias, Seidl, Thomas, Tresp, Volker
Publikováno v:
Thirty-Seventh AAAI Conference on Artificial Intelligence (AAAI-2023)
Recent transformer-based offline video instance segmentation (VIS) approaches achieve encouraging results and significantly outperform online approaches. However, their reliance on the whole video and the immense computational complexity caused by fu
Externí odkaz:
http://arxiv.org/abs/2208.10547
Video Object Segmentation (VOS) has been targeted by various fully-supervised and self-supervised approaches. While fully-supervised methods demonstrate excellent results, self-supervised ones, which do not use pixel-level ground truth, attract much
Externí odkaz:
http://arxiv.org/abs/2202.07025
Autor:
Kronberg, Elena A., Hannan, Tanveer, Huthmacher, Jens, Münzer, Marcus, Peste, Florian, Zhou, Ziyang, Berrendorf, Max, Faerman, Evgeniy, Gastaldello, Fabio, Ghizzardi, Simona, Escoubet, Philippe, Haaland, Stein, Smirnov, Artem, Sivadas, Nithin, Allen, Robert C., Tiengo, Andrea, Ilie, Raluca
The spatial distribution of energetic protons contributes towards the understanding of magnetospheric dynamics. Based upon 17 years of the Cluster/RAPID observations, we have derived machine learning-based models to predict the proton intensities at
Externí odkaz:
http://arxiv.org/abs/2105.15108