Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
OTIAS: OcTree Implicit Adaptive Sampling for Multispectral and Hyperspectral Image Fusion
Authors: Shangqi Deng, Jun Ma, Liang-Jian Deng, Ping Wei
AAAI 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Overall, our method achieves state-of-the-art performance on the CAVE and Harvard datasets with 4 and 8 scaling factors, outperforming existing approaches. ... Experimental results demonstrate that our method achieves state-of-the-art performance in the MHIF task. |
| Researcher Affiliation | Academia | 1National Key Laboratory of Human-Machine Hybrid Augmented Intelligence 2Institute of Artificial Intelligence and Robotics, Xi an Jiaotong University 3University of Electronic Science and Technology of China |
| Pseudocode | Yes | Algorithm 1: Pseudo code of OTIAS layer in a Py Torch-like style. |
| Open Source Code | Yes | Code https://github.com/shangqideng/OTIAS |
| Open Datasets | Yes | Datasets. We conduct experiments to assess the performance of our model on the CAVE1 and Harvard2. The CAVE dataset comprises 32 hyperspectral images (HSIs)... The Harvard dataset consists of 77 HSIs... 1https://www.cs.columbia.edu/CAVE/databases/multispectral/ 2http://vision.seas.harvard.edu/hyperspec/index.html |
| Dataset Splits | Yes | The simulated pairs with the associated GTs are randomly divided into training (80%) and testing (20%) sets. |
| Hardware Specification | Yes | The training epochs is fixed into 1000 on a Linux operating system with an NVIDIA RTX4090 GPU (24G). |
| Software Dependencies | Yes | The proposed network is implemented in Py Torch 2.4.0 and Python 3.11. |
| Experiment Setup | Yes | Additionally, the Adam W optimizer (P and Ba 2014) is used during training with a learning rate of 0.0001 to minimize the sum of the absolute difference (ℓ1). The training epochs is fixed into 1000 on a Linux operating system with an NVIDIA RTX4090 GPU (24G). |