Zobrazeno 1 - 8
of 8
pro vyhledávání: '"Gul, Zain"'
Autor:
Khan, Muhammad Gul Zain Ali, Naeem, Muhammad Ferjad, Van Gool, Luc, Stricker, Didier, Tombari, Federico, Afzal, Muhammad Zeshan
Continual Learning aims to learn a single model on a sequence of tasks without having access to data from previous tasks. The biggest challenge in the domain still remains catastrophic forgetting: a loss in performance on seen classes of earlier task
Externí odkaz:
http://arxiv.org/abs/2308.15827
Autor:
Naeem, Muhammad Ferjad, Khan, Muhammad Gul Zain Ali, Xian, Yongqin, Afzal, Muhammad Zeshan, Stricker, Didier, Van Gool, Luc, Tombari, Federico
Recent works have shown that unstructured text (documents) from online sources can serve as useful auxiliary information for zero-shot image classification. However, these methods require access to a high-quality source like Wikipedia and are limited
Externí odkaz:
http://arxiv.org/abs/2212.02291
Autor:
Khan, Muhammad Gul Zain Ali, Naeem, Muhammad Ferjad, Van Gool, Luc, Pagani, Alain, Stricker, Didier, Afzal, Muhammad Zeshan
Compositional zero-shot learning aims to recognize unseen compositions of seen visual primitives of object classes and their states. While all primitives (states and objects) are observable during training in some combination, their complex interacti
Externí odkaz:
http://arxiv.org/abs/2210.11557
Autor:
Ahmed, Nisar, Siddiqui, Numair A., Sanaullah, Muhammad, Jamil, Muhammad, Miraj, Muhammad Armaghan Faisal, Sajid, Zulqarnain, Gul, Zain, Kasim, Sani Ado, Imran, Qazi Sohail
Publikováno v:
In Journal of Natural Gas Geoscience February 2021 6(1):27-42
Autor:
Jianfeng Wang, Perveen Fazil, Muhammad Ishaq Ali Shah, Amir Zada, Natasha Anwar, Ghazala Gul Zain, Waliullah Khan, Farooq Jan, Tongfei Lei, Muhammad Ateeq
Publikováno v:
International Journal of Hydrogen Energy.
Autor:
Muhammad Gul Zain Ali Khan, Muhammad Ferjad Naeem, Luc Van Gool, A. Pagani, Didier Stricker, Muhammad Zeshan Afzal
Publikováno v:
2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)
Compositional zero-shot learning aims to recognize unseen compositions of seen visual primitives of object classes and their states. While all primitives (states and objects) are observable during training in some combination, their complex interacti
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::21d85bfb859c28b752f75ad547df00a1
https://hdl.handle.net/20.500.11850/593734
https://hdl.handle.net/20.500.11850/593734
Autor:
Gul, Zain1 zaingulau@gmail.com, Abdullah, Wan Hasiah2, Makeen Ahmed, Yousif M.3, Jamil, Muhammad1, Ahmed, Nisar1
Publikováno v:
Petroleum & Coal. 2020, Vol. 62 Issue 4, p1369-1388. 20p.
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.