Search Results for author: Seyed Mehdi Iranmanesh

Found 17 papers, 2 papers with code

3D Convolutional Neural Networks for Cross Audio-Visual Matching Recognition

2 code implementations18 Jun 2017 Amirsina Torfi, Seyed Mehdi Iranmanesh, Nasser M. Nasrabadi, Jeremy Dawson

We propose the use of a coupled 3D Convolutional Neural Network (3D-CNN) architecture that can map both modalities into a representation space to evaluate the correspondence of audio-visual streams using the learned multimodal features.

Speaker Verification speech-recognition +1

Empirical Upper Bound in Object Detection and More

1 code implementation27 Nov 2019 Ali Borji, Seyed Mehdi Iranmanesh

Object detection remains as one of the most notorious open problems in computer vision.

Object object-detection +1

Deep Cross Polarimetric Thermal-to-visible Face Recognition

no code implementations4 Jan 2018 Seyed Mehdi Iranmanesh, Ali Dabouei, Hadi Kazemi, Nasser M. Nasrabadi

we propose a coupled deep neural network architecture which leverages relatively large visible and thermal datasets to overcome the problem of overfitting and eventually we train it by a polarimetric thermal face dataset which is the first of its kind.

Face Recognition

Fingerprint Distortion Rectification using Deep Convolutional Neural Networks

no code implementations3 Jan 2018 Ali Dabouei, Hadi Kazemi, Seyed Mehdi Iranmanesh, Jeremi Dawson, Nasser M. Nasrabadi

Elastic distortion of fingerprints has a negative effect on the performance of fingerprint recognition systems.

Deep Sketch-Photo Face Recognition Assisted by Facial Attributes

no code implementations31 Jul 2018 Seyed Mehdi Iranmanesh, Hadi Kazemi, Sobhan Soleymani, Ali Dabouei, Nasser M. Nasrabadi

The proposed Attribute-Assisted Deep Con- volutional Neural Network (AADCNN) method exploits the facial attributes and leverages the loss functions from the facial attributes identification and face verification tasks in order to learn rich discriminative features in a common em- bedding subspace.

Attribute Face Recognition +1

ID Preserving Generative Adversarial Network for Partial Latent Fingerprint Reconstruction

no code implementations31 Jul 2018 Ali Dabouei, Sobhan Soleymani, Hadi Kazemi, Seyed Mehdi Iranmanesh, Jeremy Dawson, Nasser M. Nasrabadi

We achieved the rank-10 accuracy of 88. 02\% on the IIIT-Delhi latent fingerprint database for the task of latent-to-latent matching and rank-50 accuracy of 70. 89\% on the IIIT-Delhi MOLF database for the task of latent-to-sensor matching.

Generative Adversarial Network

Style and Content Disentanglement in Generative Adversarial Networks

no code implementations14 Nov 2018 Hadi Kazemi, Seyed Mehdi Iranmanesh, Nasser M. Nasrabadi

This paper describes the Style and Content Disentangled GAN (SC-GAN), a new unsupervised algorithm for training GANs that learns disentangled style and content representations of the data.

Disentanglement Generative Adversarial Network +1

Attribute-Guided Deep Polarimetric Thermal-to-visible Face Recognition

no code implementations27 Jul 2019 Seyed Mehdi Iranmanesh, Nasser M. Nasrabadi

In this paper, we present an attribute-guided deep coupled learning framework to address the problem of matching polarimetric thermal face photos against a gallery of visible faces.

Attribute Face Recognition +1

Attribute Adaptive Margin Softmax Loss using Privileged Information

no code implementations4 Sep 2020 Seyed Mehdi Iranmanesh, Ali Dabouei, Nasser M. Nasrabadi

We present a novel framework to exploit privileged information for recognition which is provided only during the training phase.

Attribute Face Recognition +1

HGAN: Hybrid Generative Adversarial Network

no code implementations7 Feb 2021 Seyed Mehdi Iranmanesh, Nasser M. Nasrabadi

However, GANs overlook the explicit data density characteristics which leads to undesirable quantitative evaluations and mode collapse.

Density Estimation Generative Adversarial Network

Quality-Aware Multimodal Biometric Recognition

no code implementations10 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.

PatchTrack: Multiple Object Tracking Using Frame Patches

no code implementations1 Jan 2022 Xiaotong Chen, Seyed Mehdi Iranmanesh, Kuo-Chin Lien

In this paper, we present PatchTrack, a Transformer-based joint-detection-and-tracking system that predicts tracks using patches of the current frame of interest.

Multiple Object Tracking Object

Pair DETR: Contrastive Learning Speeds Up DETR Training

no code implementations29 Oct 2022 Seyed Mehdi Iranmanesh, Xiaotong Chen, Kuo-Chin Lien

In this approach, we detect an object bounding box as a pair of keypoints, the top-left corner and the center, using two decoders.

Contrastive Learning object-detection +3

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