4 citations · 4 across the 6 of their papers we have counts for
5 papers · 1 filter
Brain-Aligned Multi-Stream Video Transformers with Sparse Self-Selection
Amir Hosein Fadaei, Mahyar Maleki, Mohammad-Reza A. Dehaqani
Modern video transformers typically ignore principles from primate vision and are rarely evaluated against neural data, limiting their biological interpretability. We introduce a s…
Enhancing Video Understanding: Deep Neural Networks for Spatiotemporal Analysis
Amir Hosein Fadaei, Mohammad-Reza A. Dehaqani
It's no secret that video has become the primary way we share information online. That's why there's been a surge in demand for algorithms that can analyze and understand video con…
Beyond still images: Temporal features and input variance resilience
Amir Hosein Fadaei, Mohammad-Reza A. Dehaqani
Traditionally, vision models have predominantly relied on spatial features extracted from static images, deviating from the continuous stream of spatiotemporal features processed b…
Spyker: High-performance Library for Spiking Deep Neural Networks
Shahriar Rezghi Shirsavar, Mohammad-Reza A. Dehaqani
Spiking neural networks (SNNs) have been recently brought to light due to their promising capabilities. SNNs simulate the brain with higher biological plausibility compared to prev…
A Faster Approach to Spiking Deep Convolutional Neural Networks
Shahriar Rezghi Shirsavar, Mohammad-Reza A. Dehaqani
Spiking neural networks (SNNs) have closer dynamics to the brain than current deep neural networks. Their low power consumption and sample efficiency make these networks interestin…