no code implementations • 31 Mar 2024 • Mainak Singha, Ankit Jha, Shirsha Bose, Ashwin Nair, Moloud Abdar, Biplab Banerjee
Central to our approach is modeling a unique prompt tailored for detecting unknown class samples, and to train this, we employ a readily accessible stable diffusion model, elegantly generating proxy images for the open class.
no code implementations • 22 Apr 2023 • Moloud Abdar, Meenakshi Kollati, Swaraja Kuraparthi, Farhad Pourpanah, Daniel McDuff, Mohammad Ghavamzadeh, Shuicheng Yan, Abduallah Mohamed, Abbas Khosravi, Erik Cambria, Fatih Porikli
Video captioning (VC) is a fast-moving, cross-disciplinary area of research that bridges work in the fields of computer vision, natural language processing (NLP), linguistics, and human-computer interaction.
1 code implementation • 11 Apr 2023 • Mehedi Hasan, Moloud Abdar, Abbas Khosravi, Uwe Aickelin, Pietro Lio', Ibrahim Hossain, Ashikur Rahman, Saeid Nahavandi
In this paper, we present a systematic review of the prediction with the reject option in the context of various neural networks.
1 code implementation • 17 Mar 2023 • Behrouz Ahadzadeh, Moloud Abdar, Fatemeh Safara, Abbas Khosravi, Mohammad Bagher Menhaj, Ponnuthurai Nagaratnam Suganthan
The results obtained indicate that the two proposed algorithms significantly outperform the other algorithms, and can be used as efficient and effective algorithms in selecting features from high-dimensional datasets.
1 code implementation • 24 Aug 2021 • Zakaria Senousy, Mohammed M. Abdelsamea, Mohamed Medhat Gaber, Moloud Abdar, U Rajendra Acharya, Abbas Khosravi, Saeid Nahavandi
It exploits the high sensitivity to the multi-level contextual information using an uncertainty quantification component to accomplish a novel dynamic ensemble model. MCUamodelhas achieved a high accuracy of 98. 11% on a breast cancer histology image dataset.
Breast Cancer Histology Image Classification Classification +2
1 code implementation • 18 May 2021 • Moloud Abdar, Soorena Salari, Sina Qahremani, Hak-Keung Lam, Fakhri Karray, Sadiq Hussain, Abbas Khosravi, U. Rajendra Acharya, Vladimir Makarenkov, Saeid Nahavandi
Differently from most of existing studies, which used either CT scan or X-ray images in COVID-19-case classification, we present a simple but efficient deep learning feature fusion model, called UncertaintyFuseNet, which is able to classify accurately large datasets of both of these types of images.
1 code implementation • 17 Nov 2020 • Farhad Pourpanah, Moloud Abdar, Yuxuan Luo, Xinlei Zhou, Ran Wang, Chee Peng Lim, Xi-Zhao Wang, Q. M. Jonathan Wu
Generalized zero-shot learning (GZSL) aims to train a model for classifying data samples under the condition that some output classes are unknown during supervised learning.
no code implementations • 12 Nov 2020 • Moloud Abdar, Farhad Pourpanah, Sadiq Hussain, Dana Rezazadegan, Li Liu, Mohammad Ghavamzadeh, Paul Fieguth, Xiaochun Cao, Abbas Khosravi, U Rajendra Acharya, Vladimir Makarenkov, Saeid Nahavandi
Uncertainty quantification (UQ) plays a pivotal role in reduction of uncertainties during both optimization and decision making processes.
no code implementations • 23 Aug 2020 • Roohallah Alizadehsani, Mohamad Roshanzamir, Sadiq Hussain, Abbas Khosravi, Afsaneh Koohestani, Mohammad Hossein Zangooei, Moloud Abdar, Adham Beykikhoshk, Afshin Shoeibi, Assef Zare, Maryam Panahiazar, Saeid Nahavandi, Dipti Srinivasan, Amir F. Atiya, U. Rajendra Acharya
We have little knowledge about the optimal treatment methods as there are many sources of uncertainty in medical science.
3 code implementations • arXiv 2020 • H M Dipu Kabir, Moloud Abdar, Seyed Mohammad Jafar Jalali, Abbas Khosravi, Amir F. Atiya, Saeid Nahavandi, Dipti Srinivasan
Traditional learning with ImageNet pre-trained initial weights and SpinalNet classification layers provided the SOTA performance on STL-10, Fruits 360, Bird225, and Caltech-101 datasets.
Ranked #1 on Satellite Image Classification on STL-10, 40 Labels
Fine-Grained Image Classification Satellite Image Classification +1
no code implementations • 17 Sep 2019 • Thang Doan, Bogdan Mazoure, Moloud Abdar, Audrey Durand, Joelle Pineau, R. Devon Hjelm
Continuous control tasks in reinforcement learning are important because they provide an important framework for learning in high-dimensional state spaces with deceptive rewards, where the agent can easily become trapped into suboptimal solutions.