2 citations · 3 across the 12 of their papers we have counts for
4 papers · 1 filter
From Filters to VLMs: Benchmarking Defogging Methods through Object Detection and Segmentation Performance
Ardalan Aryashad, Parsa Razmara, Amin Mahjoub +3
Autonomous driving perception systems are particularly vulnerable in foggy conditions, where light scattering reduces contrast and obscures fine details critical for safe operation…
PEANO-ViT: Power-Efficient Approximations of Non-Linearities in Vision Transformers
Mohammad Erfan Sadeghi, Arash Fayyazi, Seyedarmin Azizi +1
The deployment of Vision Transformers (ViTs) on hardware platforms, specially Field-Programmable Gate Arrays (FPGAs), presents many challenges, which are mainly due to the substant…
Memory-Efficient Vision Transformers: An Activation-Aware Mixed-Rank Compression Strategy
Seyedarmin Azizi, Mahdi Nazemi, Massoud Pedram
As Vision Transformers (ViTs) increasingly set new benchmarks in computer vision, their practical deployment on inference engines is often hindered by their significant memory band…
Training-Free Acceleration of ViTs with Delayed Spatial Merging
Jung Hwan Heo, Seyedarmin Azizi, Arash Fayyazi +1
Token merging has emerged as a new paradigm that can accelerate the inference of Vision Transformers (ViTs) without any retraining or fine-tuning. To push the frontier of training-…