Search Results for author: Mohammad K. Ebrahimpour

Found 7 papers, 1 papers with code

Image-based eeg classification of brain responses to song recordings

1 code implementation31 Jan 2022 Adolfo G. Ramirez-Aristizabal, Mohammad K. Ebrahimpour, Christopher T. Kello

Classifying EEG responses to naturalistic acoustic stimuli is of theoretical and practical importance, but standard approaches are limited by processing individual channels separately on very short sound segments (a few seconds or less).

Classification EEG +1

Multi-Head Deep Metric Learning Using Global and Local Representations

no code implementations28 Dec 2021 Mohammad K. Ebrahimpour, Gang Qian, Allison Beach

On the other hand, the proxy-based loss functions often lead to significant speedups in convergence during training, while the rich relations among data points are often not fully explored by the proxy-based losses.

Metric Learning Retrieval

End-to-End Auditory Object Recognition via Inception Nucleus

no code implementations25 May 2020 Mohammad K. Ebrahimpour, Timothy Shea, Andreea Danielescu, David C. Noelle, Christopher T. Kello

Machine learning approaches to auditory object recognition are traditionally based on engineered features such as those derived from the spectrum or cepstrum.

Classification General Classification +1

Ventral-Dorsal Neural Networks: Object Detection via Selective Attention

no code implementations15 May 2020 Mohammad K. Ebrahimpour, Jiayun Li, Yen-Yun Yu, Jackson L. Reese, Azadeh Moghtaderi, Ming-Hsuan Yang, David C. Noelle

The coarse functional distinction between these streams is between object recognition -- the "what" of the signal -- and extracting location related information -- the "where" of the signal.

Image Classification object-detection +2

WW-Nets: Dual Neural Networks for Object Detection

no code implementations15 May 2020 Mohammad K. Ebrahimpour, J. Ben Falandays, Samuel Spevack, Ming-Hsuan Yang, David C. Noelle

Inspired by this structure, we have proposed an object detection framework involving the integration of a "What Network" and a "Where Network".

object-detection Object Detection

FAST OBJECT LOCALIZATION VIA SENSITIVITY ANALYSIS

no code implementations ICLR 2019 Mohammad K. Ebrahimpour, David C. Noelle

We demonstrate that a simple linear mapping can be learned from sensitivity maps to bounding box coordinates, localizing the recognized object.

General Classification Image Classification +2

Image captioning with weakly-supervised attention penalty

no code implementations6 Mar 2019 Jiayun Li, Mohammad K. Ebrahimpour, Azadeh Moghtaderi, Yen-Yun Yu

Ideally, attention maps predicted by captioning models should be consistent with intrinsic attentions from visual models for any given visual concept.

Image Captioning

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