no code implementations • 28 Feb 2024 • Hadi Tabealhojeh, Soumava Kumar Roy, Peyman Adibi, Hossein Karshenas
However, performing the optimization in the Riemannian space, where the parameters and meta-parameters are located on Riemannian manifolds is computationally intensive.
no code implementations • 19 Mar 2023 • Ali Abedi, Hossein Karshenas, Peyman Adibi
To take advantage of visual relationships in caption generation, this paper proposes a deep neural network architecture for image captioning based on fusing the visual relationships information extracted from an image's scene graph with the spatial feature maps of the image.
no code implementations • 26 Nov 2021 • Maysam Behmanesh, Peyman Adibi, Mohammad Saeed Ehsani, Jocelyn Chanussot
Capturing the intra-modality and cross-modality information of multimodal data is the essential capability of multimodal learning methods.
no code implementations • 12 May 2021 • Maysam Behmanesh, Peyman Adibi, Jocelyn Chanussot, Sayyed Mohammad Saeed Ehsani
The second method is a manifold regularized multimodal classification based on pointwise correspondences (M$^2$CPC) used for the problem of multiclass classification of multimodal heterogeneous, which the correspondences between modalities are determined based on the FMBSD method.
no code implementations • 3 May 2021 • Maysam Behmanesh, Peyman Adibi, Hossein Karshenas
In this work, we propose an approach that efficiently used fuzzy rough set theory in weighted least squares twin support vector machine called FRLSTSVM for classification of imbalanced data.
no code implementations • 17 Feb 2018 • Mohammad Saeid Mahdavinejad, Mohammadreza Rezvan, Mohammadamin Barekatain, Peyman Adibi, Payam Barnaghi, Amit P. Sheth
This article assesses the different machine learning methods that deal with the challenges in IoT data by considering smart cities as the main use case.