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pro vyhledávání: '"Chowdhury, Townim Faisal"'
Autor:
Chowdhury, Townim Faisal, Liao, Kewen, Phan, Vu Minh Hieu, To, Minh-Son, Xie, Yutong, Hung, Kevin, Ross, David, Hengel, Anton van den, Verjans, Johan W., Liao, Zhibin
Deep Neural Networks (DNNs) are widely used for visual classification tasks, but their complex computation process and black-box nature hinder decision transparency and interpretability. Class activation maps (CAMs) and recent variants provide ways t
Externí odkaz:
http://arxiv.org/abs/2404.02388
Autor:
Shubho, Fahimul Hoque, Chowdhury, Townim Faisal, Cheraghian, Ali, Saberi, Morteza, Mohammed, Nabeel, Rahman, Shafin
Zero-shot learning (ZSL) aims to classify objects that are not observed or seen during training. It relies on class semantic description to transfer knowledge from the seen classes to the unseen classes. Existing methods of obtaining class semantics
Externí odkaz:
http://arxiv.org/abs/2310.11657
Autor:
Nasiri, Majid, Cheraghian, Ali, Chowdhury, Townim Faisal, Ahmadi, Sahar, Saberi, Morteza, Rahman, Shafin
Zero-shot learning on 3D point cloud data is a related underexplored problem compared to its 2D image counterpart. 3D data brings new challenges for ZSL due to the unavailability of robust pre-trained feature extraction models. To address this proble
Externí odkaz:
http://arxiv.org/abs/2209.14690
Autor:
Hossain, Md Sazzad, Saha, Pritom, Chowdhury, Townim Faisal, Rahman, Shafin, Rahman, Fuad, Mohammed, Nabeel
It is common to have continuous streams of new data that need to be introduced in the system in real-world applications. The model needs to learn newly added capabilities (future tasks) while retaining the old knowledge (past tasks). Incremental lear
Externí odkaz:
http://arxiv.org/abs/2205.11367
Akademický článek
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Publikováno v:
Circulation (Ovid); November 2021, Vol. 144 Issue: Supplement 1 pA12546-A12546, 1p