From the 1 of 14 linked papers with an AI index.
14 papers
WHTMix: Efficient Stereo Depth Estimation via Walsh-Hadamard Token Mixing
Prathyush Sajith, Emadeldeen Hamdan, Ahmet Enis Cetin
The paper proposes replacing the global self‑attention stage in stereo transformer models with a data‑independent Walsh‑Hadamard token mixer, achieving similar depth accuracy while…
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 Imaging with Quantum Detectors
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…
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…
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…
Deep Learning Based Wildfire Detection for Peatland Fires Using Transfer Learning
Emadeldeen Hamdan, Ahmad Faiz Tharima, Mohd Zahirasri Mohd Tohir +4
Machine learning (ML)-based wildfire detection methods have been developed in recent years, primarily using deep learning (DL) models trained on large collections of wildfire image…