no code implementations • ECCV 2020 • Fariborz Taherkhani, Ali Dabouei, Sobhan Soleymani, Jeremy Dawson, Nasser M. Nasrabadi
Semi-Supervised Learning (SSL) based on Convolutional Neural Networks (CNNs) have recently been proven as powerful tools for standard tasks such as image classification when there is not a sufficient amount of labeled data available during the training.
no code implementations • 2 Sep 2022 • Ali Dabouei, Fariborz Taherkhani, Sobhan Soleymani, Nasser M. Nasrabadi
This phenomenon hinders the outer optimization in AT since the convergence rate of MSGD is highly dependent on the variance of the gradients.
1 code implementation • 30 Aug 2022 • Fariborz Taherkhani, Aashish Rai, Quankai Gao, Shaunak Srivastava, Xuanbai Chen, Fernando de la Torre, Steven Song, Aayush Prakash, Daeil Kim
3D face modeling has been an active area of research in computer vision and computer graphics, fueling applications ranging from facial expression transfer in virtual avatars to synthetic data generation.
no code implementations • 10 Dec 2021 • Sobhan Soleymani, Ali Dabouei, Fariborz Taherkhani, Seyed Mehdi Iranmanesh, Jeremy Dawson, Nasser M. Nasrabadi
The first loss assures that the representations of modalities for a class have comparable magnitudes to provide a better quality estimation, while the multimodal representations of different classes are distributed to achieve maximum discrimination in the embedding space.
no code implementations • 29 Jul 2021 • Fariborz Taherkhani, Ali Dabouei, Sobhan Soleymani, Jeremy Dawson, Nasser M. Nasrabadi
To address this problem, we use a new Multi-Task Learning (MTL) paradigm in which a facial attribute predictor uses the knowledge of other related attributes to obtain a better generalization performance.
no code implementations • 29 Jul 2021 • Fariborz Taherkhani, Veeru Talreja, Jeremy Dawson, Matthew C. Valenti, Nasser M. Nasrabadi
We have evaluated the performance of cpCNN and ADDA and compared it with the proposed cpGAN.
no code implementations • 29 Jul 2021 • Fariborz Taherkhani, Ali Dabouei, Sobhan Soleymani, Jeremy Dawson, Nasser M. Nasrabadi
In this paper, we propose a new method to leverage features from human attributes for person ReID.
no code implementations • CVPR 2021 • Fariborz Taherkhani, Ali Dabouei, Sobhan Soleymani, Jeremy Dawson, Nasser M. Nasrabadi
The goal is to use Wasserstein metric to provide pseudo labels for the unlabeled images to train a Convolutional Neural Networks (CNN) in a Semi-Supervised Learning (SSL) manner for the classification task.
no code implementations • 4 Dec 2020 • Fariborz Taherkhani, Hadi Kazemi, Ali Dabouei, Jeremy Dawson, Nasser M. Nasrabadi
Semi-Supervised Learning (SSL) approaches have been an influential framework for the usage of unlabeled data when there is not a sufficient amount of labeled data available over the course of training.
no code implementations • 2 Dec 2020 • Sobhan Soleymani, Ali Dabouei, Fariborz Taherkhani, Jeremy Dawson, Nasser M. Nasrabadi
The network is trained by triplets of face images, in which the intermediate image inherits the landmarks from one image and the appearance from the other image.
no code implementations • 9 Oct 2020 • Moktari Mostofa, Fariborz Taherkhani, Jeremy Dawson, Nasser M. Nasrabadi
Cross-spectral iris recognition is emerging as a promising biometric approach to authenticating the identity of individuals.
no code implementations • 25 Apr 2020 • Fariborz Taherkhani, Veeru Talreja, Jeremy Dawson, Matthew C. Valenti, Nasser M. Nasrabadi
In this paper, we hypothesize that the profile face domain possesses a gradual connection with the frontal face domain in the deep feature space.
no code implementations • 3 Apr 2020 • Fariborz Taherkhani, Veeru Talreja, Matthew C. Valenti, Nasser M. Nasrabadi
The DNDCMH network consists of two separatecomponents: an attribute-based deep cross-modal hashing (ADCMH) module, which uses a margin (m)-based loss function toefficiently learn compact binary codes to preserve similarity between modalities in the Hamming space, and a neural error correctingdecoder (NECD), which is an error correcting decoder implemented with a neural network.
2 code implementations • CVPR 2021 • Ali Dabouei, Sobhan Soleymani, Fariborz Taherkhani, Nasser M. Nasrabadi
On the distillation task, solely classifying images mixed using the teacher's knowledge achieves comparable performance to the state-of-the-art distillation methods.
no code implementations • 13 Jan 2020 • Ali Dabouei, Fariborz Taherkhani, Sobhan Soleymani, Jeremy Dawson, Nasser M. Nasrabadi
We demonstrate that the proposed approach enhances the performance of deep face recognition models by assisting the training process in two ways.
1 code implementation • 8 Oct 2019 • Ali Dabouei, Sobhan Soleymani, Fariborz Taherkhani, Jeremy Dawson, Nasser M. Nasrabadi
Deep neural networks are susceptible to adversarial manipulations in the input domain.
no code implementations • ICCV 2019 • Fariborz Taherkhani, Hadi Kazemi, Ali Dabouei, Jeremy Dawson, Nasser M. Nasrabadi
Our deep model uses coarse images in conjunction with fine images to jointly explore the low rank and sparse subspaces by sharing the parameters during the training which causes the data points obtained by the CNN to be well-projected to both sparse and low rank subspaces for classification.
no code implementations • 17 Sep 2019 • Hadi Kazemi, Fariborz Taherkhani, Nasser M. Nasrabadi
First, we propose a multi-scale generator architecture for face hallucination with a high up-scaling ratio factor, which has multiple intermediate outputs at different resolutions.
no code implementations • 15 Aug 2019 • Fariborz Taherkhani, Jeremy Dawson, Nasser M. Nasrabadi
In this book chapter, we propose a new Convolutional Neural Network (CNN) framework which adopts a structural sparsity learning technique to select the optimal spectral bands to obtain the best face recognition performance over all of the spectral bands.
no code implementations • 5 Aug 2019 • Veeru Talreja, Fariborz Taherkhani, Matthew C. Valenti, Nasser M. Nasrabadi
In this paper, we propose a novel attribute-guided cross-resolution (low-resolution to high-resolution) face recognition framework that leverages a coupled generative adversarial network (GAN) structure with adversarial training to find the hidden relationship between the low-resolution and high-resolution images in a latent common embedding subspace.
no code implementations • 11 Feb 2019 • Veeru Talreja, Fariborz Taherkhani, Matthew C. Valenti, Nasser M. Nasrabadi
With benefits of fast query speed and low storage cost, hashing-based image retrieval approaches have garnered considerable attention from the research community.
no code implementations • NeurIPS 2018 • Hadi Kazemi, Sobhan Soleymani, Fariborz Taherkhani, Seyed Mehdi Iranmanesh, Nasser M. Nasrabadi
These approaches usually fail to model domain-specific information which has no representation in the target domain.
no code implementations • 12 Oct 2018 • Hadi Kazemi, Fariborz Taherkhani, Nasser M. Nasrabadi
In contrast to current unsupervised image-to-image translation techniques, our framework leverages a novel perceptual discriminator to learn the geometry of human face.
no code implementations • 20 Apr 2018 • Fariborz Taherkhani, Nasser M. Nasrabadi, Jeremy Dawson
In this paper, we propose a new deep framework which predicts facial attributes and leverage it as a soft modality to improve face identification performance.
no code implementations • 31 May 2017 • Fariborz Taherkhani
This paper has been removed from arXiv as the submitter did not have ownership of the data presented in this work.
no code implementations • 22 Jul 2016 • Fariborz Taherkhani
Second, the sequence of these ordered patches is modeled as a probabilistic feature vector by CRF to model spatial relationship of these local properties.
no code implementations • 22 Jul 2016 • Fariborz Taherkhani, Reza Hedayati
In this work, we propose an efficient image representation model for classification.