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pro vyhledávání: '"Ward, Isaac Ronald"'
Contrastive learning has recently demonstrated superior performance to supervised learning, despite requiring no training labels. We explore how contrastive learning can be applied to hundreds of thousands of unlabeled Mars terrain images, collected
Externí odkaz:
http://arxiv.org/abs/2210.09234
Autor:
Ward, Isaac Ronald, Wang, Ling, lu, Juan, Bennamoun, Mohammed, Dwivedi, Girish, Sanfilippo, Frank M
Explainable Artificial Intelligence (XAI) has been identified as a viable method for determining the importance of features when making predictions using Machine Learning (ML) models. In this study, we created models that take an individual's health
Externí odkaz:
http://arxiv.org/abs/2112.13210
Graph neural networks (GNNs) have recently grown in popularity in the field of artificial intelligence (AI) due to their unique ability to ingest relatively unstructured data types as input data. Although some elements of the GNN architecture are con
Externí odkaz:
http://arxiv.org/abs/2010.05234
Object detection from RGB images is a long-standing problem in image processing and computer vision. It has applications in various domains including robotics, surveillance, human-computer interaction, and medical diagnosis. With the availability of
Externí odkaz:
http://arxiv.org/abs/1907.09236
This work investigates the impact of the loss function on the performance of Neural Networks, in the context of a monocular, RGB-only, image localization task. A common technique used when regressing a camera's pose from an image is to formulate the
Externí odkaz:
http://arxiv.org/abs/1905.03692
Autor:
Allaert, Benjamin, Ward, Isaac Ronald, Bilasco, Ioan Marius, Djeraba, Chaabane, Bennamoun, Mohammed
Optical flow techniques are becoming increasingly performant and robust when estimating motion in a scene, but their performance has yet to be proven in the area of facial expression recognition. In this work, a variety of optical flow approaches are
Externí odkaz:
http://arxiv.org/abs/1904.11592
Autor:
WARD, ISAAC RONALD1,2 isaacronaldward@gmail.com, JOYNER, JACK1 jack@isolabs.com.au, LICKFOLD, CASEY1 casey@isolabs.com.au, YULAN GUO3 yulan.guo@nudt.edu.cn, BENNAMOUN, MOHAMMED4 mohammed.bennamoun@uwa.edu.au
Publikováno v:
ACM Computing Surveys. 2022 Suppl 10, Vol. 54, p1-35. 35p.
Autor:
Allaert, Benjamin, Ward, Isaac Ronald, Bilasco, Ioan Marius, Djeraba, Chaabane, Bennamoun, Mohammed
Publikováno v:
Neurocomputing
Neurocomputing, 2022, ⟨10.1016/j.neucom.2022.05.077⟩
Neurocomputing, 2022, ⟨10.1016/j.neucom.2022.05.077⟩
International audience
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::cf002b6f46cc405046c89cf9abc053a1
https://hal.science/hal-03684137
https://hal.science/hal-03684137
Autor:
Allaert, Benjamin, Ward, Isaac Ronald, Bilasco, Ioan Marius, Djeraba, Chaabane, Bennamoun, Mohammed
Optical flow techniques are becoming increasingly performant and robust when estimating motion in a scene, but their performance has yet to be proven in the area of facial expression recognition. In this work, a variety of optical flow approaches are
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::da8e17a2c1f75f379f34653ba3bf6efd
https://hal.science/hal-02110143
https://hal.science/hal-02110143
Publikováno v:
RGB-D Image Analysis and Processing ISBN: 9783030286026
Object detection from RGB images is a long-standing problem in image processing and computer vision. It has applications in various domains including robotics, surveillance, human–computer interaction, and medical diagnosis. With the availability o
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1e3326a6deb80d88c1b2832cfd947048
https://hdl.handle.net/11541.2/139991
https://hdl.handle.net/11541.2/139991