no code implementations • 6 Jun 2024 • Azadeh Alavi, Sanduni Jayasinghe
Realtime finite element modeling of bridges assists modern structural health monitoring systems by providing comprehensive insights into structural integrity.
1 code implementation • 14 Mar 2024 • Ziyu Liu, Azadeh Alavi, Minyi Li, Xiang Zhang
In this paper, we will present a comprehensive comparative study between contrastive and generative methods in time series.
no code implementations • 5 Oct 2021 • Azadeh Alavi
In order to develop a self monitoring mobile application, in this work, we propose a novel deep subspace analysis pipeline for semi-supervised diabetic foot ulcer mulit-label classification.
no code implementations • 5 Oct 2021 • Azadeh Alavi, Hossein Akhoundi
As such, we merge the pre-trained Xception network with a multi-class variational classifier.
no code implementations • 7 Oct 2020 • Moi Hoon Yap, Ryo Hachiuma, Azadeh Alavi, Raphael Brungel, Bill Cassidy, Manu Goyal, Hongtao Zhu, Johannes Ruckert, Moshe Olshansky, Xiao Huang, Hideo Saito, Saeed Hassanpour, Christoph M. Friedrich, David Ascher, Anping Song, Hiroki Kajita, David Gillespie, Neil D. Reeves, Joseph Pappachan, Claire O'Shea, Eibe Frank
DFUC2020 provided participants with a comprehensive dataset consisting of 2, 000 images for training and 2, 000 images for testing.
no code implementations • 12 Apr 2018 • Hongyu Xu, Jingjing Zheng, Azadeh Alavi, Rama Chellappa
These intermediate domains form a smooth path and bridge the gap between the source and target domains.
no code implementations • 16 Feb 2017 • Amit Kumar, Azadeh Alavi, Rama Chellappa
In this paper, we show that without using any 3D information, KEPLER outperforms state of the art methods for alignment on challenging datasets such as AFW and AFLW.
Ranked #16 on Head Pose Estimation on BIWI
no code implementations • 14 Jun 2016 • Maya Kabkab, Azadeh Alavi, Rama Chellappa
Large-scale supervised classification algorithms, especially those based on deep convolutional neural networks (DCNNs), require vast amounts of training data to achieve state-of-the-art performance.
2 code implementations • 19 Apr 2016 • Swami Sankaranarayanan, Azadeh Alavi, Carlos Castillo, Rama Chellappa
Despite significant progress made over the past twenty five years, unconstrained face verification remains a challenging problem.
Ranked #11 on Face Verification on IJB-A
no code implementations • 10 Feb 2016 • Azadeh Alavi, Vishal M. Patel, Rama Chellappa
Recently, it was shown that embedding such manifolds into a Random Projection Spaces (RPS), rather than RKHS or tangent space, leads to higher classification and clustering performance.
no code implementations • 10 Feb 2016 • Swami Sankaranarayanan, Azadeh Alavi, Rama Chellappa
In this work, we present an unconstrained face verification algorithm and evaluate it on the recently released IJB-A dataset that aims to push the boundaries of face verification methods.
no code implementations • 18 Sep 2015 • Kun Zhao, Azadeh Alavi, Arnold Wiliem, Brian C. Lovell
We then validate our framework on several computer vision applications by comparing against popular clustering methods on Riemannian manifolds.
no code implementations • 4 Mar 2014 • Azadeh Alavi, Yan Yang, Mehrtash Harandi, Conrad Sanderson
The use of similarity vectors is in contrast to the traditional approach of embedding manifolds into tangent spaces, which can suffer from representing the manifold structure inaccurately.
no code implementations • 4 Mar 2014 • Azadeh Alavi, Arnold Wiliem, Kun Zhao, Brian C. Lovell, Conrad Sanderson
Recent advances suggest that encoding images through Symmetric Positive Definite (SPD) matrices and then interpreting such matrices as points on Riemannian manifolds can lead to increased classification performance.