18 citations · 29 across the 5 of their papers we have counts for
6 papers
FTerViT: Fully Ternary Vision Transformer
Szymon Ruciński, Pietro Bonazzi, Engin Türetken +3
Ternary Vision Transformers offer substantial model compression, however state-of-the-art methods only ternarize the encoder layers, leaving patch embeddings, LayerNorm parameters,…
Optimizing IoT-Based Asset and Utilization Tracking: Efficient Activity Classification with MiniRocket on Resource-Constrained Devices
Marco Giordano, Silvano Cortesi, Michele Crabolu +6
This paper introduces an effective solution for retrofitting construction power tools with low-power IoT to enable accurate activity classification. We address the challenge of dis…
Adaptation of MobileNetV2 for Face Detection on Ultra-Low Power Platform
Simon Narduzzi, Engin Türetken, Jean-Philippe Thiran +1
Designing Deep Neural Networks (DNNs) running on edge hardware remains a challenge. Standard designs have been adopted by the community to facilitate the deployment of Neural Netwo…
Efficient Blind-Spot Neural Network Architecture for Image Denoising
David Honzátko, Siavash A. Bigdeli, Engin Türetken +1
Image denoising is an essential tool in computational photography. Standard denoising techniques, which use deep neural networks at their core, require pairs of clean and noisy ima…
Embedded Deep Learning for Sleep Staging
Engin Türetken, Jérôme Van Zaen, Ricard Delgado-Gonzalo
The rapidly-advancing technology of deep learning (DL) into the world of the Internet of Things (IoT) has not fully entered in the fields of m-Health yet. Among the main reasons ar…
Globally Optimal Cell Tracking using Integer Programming
Engin Türetken, Xinchao Wang, Carlos Becker +2
We propose a novel approach to automatically tracking cell populations in time-lapse images. To account for cell occlusions and overlaps, we introduce a robust method that generate…