3 citations · 7 across the 24 of their papers we have counts for
6 papers · 2 filters
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…