3 citations · 7 across the 24 of their papers we have counts for
11 papers · 1 filter
WHTMix: Efficient Stereo Depth Estimation via Walsh-Hadamard Token Mixing
Prathyush Sajith, Emadeldeen Hamdan, Ahmet Enis Cetin
Stereo depth estimation for driving, robotics and augmented reality must run at high resolution under tight latency budgets, yet in transformer-based matchers the global self-atten…
ShearFuse-UNet: Hadamard, DCT, and Shearlet Transform Fusion for Next-Day Wildfire Spread Prediction
Ene Meco, Yingyi Luo, Emadeldeen Hamdan +2
We propose ShearFuse-UNet, a lightweight and computationally efficient deep learning model for next-day wildfire spread prediction from multi-modal satellite data. The model integr…
DynGhost: Temporally-Modelled Transformer for Dynamic Ghost Imagings
Vittorio Palladino, Ahmet Enis Cetin
Ghost imaging reconstructs spatial information from a single-pixel bucket detector by correlating structured illumination patterns with scalar intensity measurements. While deep le…
WTHaar-Net: a Hybrid Quantum-Classical Approach
Vittorio Palladino, Tsai Idden, Ahmet Enis Cetin
Convolutional neural networks rely on linear filtering operations that can be reformulated efficiently in suitable transform domains. At the same time, advances in quantum computin…
Toward Generalizable Deep Learning Based Peatland Fire Detection via Walsh Hadamard Transform and Domain Adaptation
Emadeldeen Hamdan, Ahmad Faiz Tharima, Mohd Zahirasri Mohd Tohir +4
Machine learning-based wildfire detection has advanced significantly using deep learning models trained on large wildfire image and video datasets. However, peatland fires exhibit…
U-Net with Hadamard Transform and DCT Latent Spaces for Next-day Wildfire Spread Prediction
Yingyi Luo, Shuaiang Rong, Adam Watts +1
We developed a lightweight and computationally efficient tool for next-day wildfire spread prediction using multimodal satellite data as input. The deep learning model, which we ca…
HTMA-Net: Towards Multiplication-Avoiding Neural Networks via Hadamard Transform and In-Memory Computing
Emadeldeen Hamdan, Ahmet Enis Cetin
Reducing the cost of multiplications is critical for efficient deep neural network deployment, especially in energy-constrained edge devices. In this work, we introduce HTMA-Net, a…
Shifts in Doctors' Eye Movements Between Real and AI-Generated Medical Images
David C Wong, Bin Wang, Gorkem Durak +21
Eye-tracking analysis plays a vital role in medical imaging, providing key insights into how radiologists visually interpret and diagnose clinical cases. In this work, we first ana…
Eyes Tell the Truth: GazeVal Highlights Shortcomings of Generative AI in Medical Imaging
David Wong, Bin Wang, Gorkem Durak +23
The demand for high-quality synthetic data for model training and augmentation has never been greater in medical imaging. However, current evaluations predominantly rely on computa…
Discrete Cosine Transform Based Decorrelated Attention for Vision Transformers
Hongyi Pan, Emadeldeen Hamdan, Xin Zhu +2
Self-attention is central to the success of Transformer architectures; however, learning the query, key, and value projections from random initialization remains challenging and co…
Wildfire Detection Via Transfer Learning: A Survey
Ziliang Hong, Emadeldeen Hamdan, Yifei Zhao +3
This paper surveys different publicly available neural network models used for detecting wildfires using regular visible-range cameras which are placed on hilltops or forest lookou…