activity
20172021
most citedFew-Shot Adversarial Domain Adaptation

207 citations · 353 across the 6 of their papers we have counts for

collaborators

15 papers

cs.CV2021

HGAN: Hybrid Generative Adversarial Network

Seyed Mehdi Iranmanesh, Nasser M. Nasrabadi

In this paper, we present a simple approach to train Generative Adversarial Networks (GANs) in order to avoid a \textit {mode collapse} issue. Implicit models such as GANs tend to…

cs.CV20208 cited

Attribute Adaptive Margin Softmax Loss using Privileged Information

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. Here, we focus on recognition task where images are…

cs.CV2020

Robust Facial Landmark Detection via Aggregation on Geometrically Manipulated Faces

Seyed Mehdi Iranmanesh, Ali Dabouei, Sobhan Soleymani +2

In this work, we present a practical approach to the problem of facial landmark detection. The proposed method can deal with large shape and appearance variations under the rich sh…

cs.CV201919 cited

Empirical Upper Bound in Object Detection and More

Ali Borji, Seyed Mehdi Iranmanesh

Object detection remains as one of the most notorious open problems in computer vision. Despite large strides in accuracy in recent years, modern object detectors have started to s…

cs.CV2019

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

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…

cs.CV2018

Unsupervised Image-to-Image Translation Using Domain-Specific Variational Information Bound

Hadi Kazemi, Sobhan Soleymani, Fariborz Taherkhani +2

Unsupervised image-to-image translation is a class of computer vision problems which aims at modeling conditional distribution of images in the target domain, given a set of unpair…