Zobrazeno 1 - 10
of 165
pro vyhledávání: '"Mustansar Fiaz"'
Publikováno v:
IEEE Access, Vol 8, Pp 100857-100869 (2020)
Template based learning, particularly Siamese networks, has recently become popular due to balancing accuracy and speed. However, preserving tracker robustness against challenging scenarios with real-time speed is a primary concern for visual object
Externí odkaz:
https://doaj.org/article/19de3e4073fb4530ba6d1bdf4039e071
Publikováno v:
2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b90ff9a5939252f10e27049af8e17fc9
https://doi.org/10.1109/wacv56688.2023.00480
https://doi.org/10.1109/wacv56688.2023.00480
Publikováno v:
Image Analysis ISBN: 9783031314377
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::46f04a51237376264637470a76762b2a
https://doi.org/10.1007/978-3-031-31438-4_39
https://doi.org/10.1007/978-3-031-31438-4_39
Publikováno v:
Computer Vision – ACCV 2022 ISBN: 9783031263477
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::4f37d5007cbb1a9b9d326486c6615139
https://doi.org/10.1007/978-3-031-26348-4_14
https://doi.org/10.1007/978-3-031-26348-4_14
Publikováno v:
Sensors, Vol 20, Iss 13, p 3780 (2020)
CNN-based trackers, especially those based on Siamese networks, have recently attracted considerable attention because of their relatively good performance and low computational cost. For many Siamese trackers, learning a generic object model from a
Externí odkaz:
https://doaj.org/article/7cbf72e84a82463a90902c350001cc63
Publikováno v:
Sensors, Vol 20, Iss 14, p 4021 (2020)
We propose to improve the visual object tracking by introducing a soft mask based low-level feature fusion technique. The proposed technique is further strengthened by integrating channel and spatial attention mechanisms. The proposed approach is int
Externí odkaz:
https://doaj.org/article/2007a112c06445ebac3e36c69a0440b9
Publikováno v:
IEEE Access, Vol 8, Pp 100857-100869 (2020)
Template based learning, particularly Siamese networks, has recently become popular due to balancing accuracy and speed. However, preserving tracker robustness against challenging scenarios with real-time speed is a primary concern for visual object
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9b6fa8bfa2f463bc23a1d9742d582790
https://ieeexplore.ieee.org/document/9102303/
https://ieeexplore.ieee.org/document/9102303/
Autor:
Mustansar Fiaz, Arif Mahmood, Sehar Shahzad Farooq, Kamran Ali, Muhammad Shaheryar, Soon Ki Jung
Publikováno v:
Communications in Computer and Information Science ISBN: 9783031063800
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c92e5f56b2e2069221dac68c58924863
https://doi.org/10.1007/978-3-031-06381-7_14
https://doi.org/10.1007/978-3-031-06381-7_14
Autor:
Sehar Shahzad Farooq, Ali Kashif Bashir, Raheel Nawaz, Mustansar Fiaz, Irfan Mehmood, Soon Ki Jung, Kyung-Joong Kim
Game player modeling is a paradigm of computational models to exploit players’ behavior and experience using game and player analytics. Player modeling refers to descriptions of players based on frameworks of data derived from the interaction of a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::82af9c4b9096ef150d703f1b22ea08f2
https://e-space.mmu.ac.uk/627816/
https://e-space.mmu.ac.uk/627816/
Autor:
Kyung-Joong Kim, Sang Bin Yun, Ki Yeol Baek, Sehar Shahzad Farooq, Naram Choi, Hammad Mukhtar, Abdullah Aziz, Soon Ki Jung, Mustansar Fiaz
Publikováno v:
Communications in Computer and Information Science ISBN: 9783030816377
IW-FCV
IW-FCV
Games has been considered as a benchmark for practicing computational models to analyze players interest as well as its involvement in the game. Though several aspects of game related research are carried out in different fields of research including
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::56bd2568e9fb1a395d3f616fcd150f38
https://doi.org/10.1007/978-3-030-81638-4_5
https://doi.org/10.1007/978-3-030-81638-4_5