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Arman Afrasiyabi

6 accepted papers

2025

Latent Representation Learning for Multimodal Brain Activity Translation

Arman Afrasiyabi, Dhananjay Bhaskar, Erica L. Busch, Laurent Caplette, Rahul Singh, Guillaume Lajoie +2
ICASSP 2025accepted

Neuroscience employs diverse neuroimaging techniques, each offering distinct insights into brain activity, from electrophysiological recordings such as EEG, which have high temporal resolution, to hemodynamic modalities such as fMRI, which have increased spatial precision. However, integrating these…

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2023

DarSwin: Distortion Aware Radial Swin Transformer

Akshaya Athwale, Arman Afrasiyabi, Justin Lagüe, Ichrak Shili, Ola Ahmad, Jean-François Lalonde
ICCV 2023poster

Wide-angle lenses are commonly used in perception tasks requiring a large field of view. Unfortunately, these lenses produce significant distortions making conventional models that ignore the distortion effects unable to adapt to wide-angle images. In this paper, we present a novel transformer-based…

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2022

Matching Feature Sets for Few-Shot Image Classification

Arman Afrasiyabi, Hugo Larochelle, Jean-François Lalonde, Christian Gagné
CVPR 2022poster

In image classification, it is common practice to train deep networks to extract a single feature vector per input image. Few-shot classification methods also mostly follow this trend. In this work, we depart from this established direction and instead propose to extract sets of feature vectors for…

Cited by 127PDF ↗Scholar ↗
2021

Mixture-Based Feature Space Learning for Few-Shot Image Classification

Arman Afrasiyabi, Jean-François Lalonde, Christian Gagné
ICCV 2021poster

We introduce Mixture-based Feature Space Learning (MixtFSL) for obtaining a rich and robust feature representation in the context of few-shot image classification. Previous works have proposed to model each base class either with a single point or with a mixture model by relying on offline clusterin…

Cited by 105PDF ↗Scholar ↗
2020

Associative Alignment for Few-shot Image Classification

Arman Afrasiyabi, Jean-François Lalonde, Christian Gagné
ECCV 2020poster

Few-shot image classification aims at training a model from only a few examples for each of the ``novel'' classes. This paper proposes the idea of associative alignment for leveraging part of the base data by aligning the novel training instances to the closely related ones in the base training set.…

Cited by 181Source ↗PDF ↗Scholar ↗
2018

Non-Euclidean Vector Product for Neural Networks

Arman Afrasiyabi, Diaa Badawi, Baris Nasir, Ozan Yildiz, Fatos T. Yarman-Vural, A. Enis Çetin
ICASSP 2018accepted

We present a non-Euclidean vector product for artificial neural networks. The vector product operator does not require any multiplications while providing correlation information between two vectors. Ordinary neurons require inner product of two vectors. We propose a class of neural networks with th…

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