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pro vyhledávání: '"Madan, Neelu"'
Video Foundation Models (ViFMs) aim to learn a general-purpose representation for various video understanding tasks. Leveraging large-scale datasets and powerful models, ViFMs achieve this by capturing robust and generic features from video data. Thi
Externí odkaz:
http://arxiv.org/abs/2405.03770
Autor:
Madan, Neelu, Ristea, Nicolae-Catalin, Nasrollahi, Kamal, Moeslund, Thomas B., Ionescu, Radu Tudor
Masked image modeling has been demonstrated as a powerful pretext task for generating robust representations that can be effectively generalized across multiple downstream tasks. Typically, this approach involves randomly masking patches (tokens) in
Externí odkaz:
http://arxiv.org/abs/2308.16572
Autor:
Madan, Neelu, Ristea, Nicolae-Catalin, Ionescu, Radu Tudor, Nasrollahi, Kamal, Khan, Fahad Shahbaz, Moeslund, Thomas B., Shah, Mubarak
Anomaly detection has recently gained increasing attention in the field of computer vision, likely due to its broad set of applications ranging from product fault detection on industrial production lines and impending event detection in video surveil
Externí odkaz:
http://arxiv.org/abs/2209.12148
Autor:
Ristea, Nicolae-Catalin, Madan, Neelu, Ionescu, Radu Tudor, Nasrollahi, Kamal, Khan, Fahad Shahbaz, Moeslund, Thomas B., Shah, Mubarak
Anomaly detection is commonly pursued as a one-class classification problem, where models can only learn from normal training samples, while being evaluated on both normal and abnormal test samples. Among the successful approaches for anomaly detecti
Externí odkaz:
http://arxiv.org/abs/2111.09099
Akademický článek
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Akademický článek
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Publikováno v:
Madan, N, Nasrollahi, K & Moeslund, T B 2022, Attention-Enabled Object Detection to Improve One-Stage Tracker . in K Arai (ed.), Intelligent Systems and Applications : Proceedings of the 2021 Intelligent Systems Conference (IntelliSys) Volume 1 . vol. 2, Springer, Lecture Notes in Networks and Systems, vol. 295, pp. 736-754, Intelligent Systems Conference 2021, Amsterdam, Netherlands, 02/09/2021 . https://doi.org/10.1007/978-3-030-82196-8_55
State-of-the-art (SoTA) detection-based tracking methods mostly accomplish the detection and the identification feature learning tasks separately. Only a few efforts include the joint learning of detection and identification features. This work propo
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::2b5004615e957fab4004f8299248831e
https://vbn.aau.dk/da/publications/461f6b44-4118-4e5e-96bf-06a3ad2042b5
https://vbn.aau.dk/da/publications/461f6b44-4118-4e5e-96bf-06a3ad2042b5
Autor:
Malik N; Department of Obstetrics and Gynaecology, Lady Hardinge Medical College and Smt Sucheta Kriplani Hospital, New Delhi 110001., Raghunandan C, Madan N
Publikováno v:
Journal of the Indian Medical Association [J Indian Med Assoc] 2002 Nov; Vol. 100 (11), pp. 646, 648, 650-1.
