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..
WKV-sharing embraced random shuffle RWKV high-order modeling for pan-sharpening
Authors: man zhou, Xuanhua He, Danfeng Hong, Bo Huang
NeurIPS 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Comprehensive experiments across pan-sharpening benchmarks demonstrate our model s effectiveness, consistently outperforming state-of-the-art alternatives. (Abstract) and 3 Experiments over pan-sharpening To evaluate the performance, we conduct comparative analysis against pan-sharpening. The traditional methods included SFIM [1], Brovey [2], GS [3], IHS [4], and GFPCA [5]. Additionally, we include various deep learning-based techniques, such as PNN [6], PANNET [7], MSDCNN [8], SRPPNN [9], GPPNN [10], Mut Net [11], INNformer [12], SFINet [13], and Pan Flow Net [14]. |
| Researcher Affiliation | Academia | Man Zhou, Xuanhua He1, Danfeng Hong2, Bo Huang3 1 University of Science and Technology of China 2 Southeast University 3 The University of Hong Kong |
| Pseudocode | No | The paper describes the proposed method using textual explanations and mathematical equations (e.g., equations (1)-(20) and (21)-(24)) but does not include any explicitly labeled 'Pseudocode' or 'Algorithm' blocks, nor structured code-like procedures. |
| Open Source Code | No | In the 'NeurIPS Paper Checklist', under question '5. Open access to data and code', the answer is '[Yes]' with the justification '[TODO]', indicating that the authors intended to provide a justification for code release but it is missing. The main body of the paper does not contain any explicit statement about code availability or a link to a repository. |
| Open Datasets | Yes | Table 1: Comparison on the Word View-II, Word View-III and Gao Fen2 datasets. The paper explicitly names 'Word View-II', 'Word View-III', and 'Gao Fen2' as datasets used for evaluation, which are established benchmarks in pan-sharpening. |
| Dataset Splits | No | The paper's NeurIPS checklist claims that training and test details, including data splits, can be found on 'page A.5'. However, 'page A.5' is not included in the provided text of the research paper. Therefore, explicit dataset split information is not available in the given document. |
| Hardware Specification | No | In the 'NeurIPS Paper Checklist', under question '8. Experiments compute resources', the answer is '[Yes]' with the justification '[TODO]', indicating that the authors intended to provide details but it is missing. The main body of the paper does not specify any particular GPU models, CPU types, or other hardware used for the experiments. |
| Software Dependencies | No | The paper's NeurIPS checklist claims that training and test details can be found on 'page A.5'. However, 'page A.5' is not included in the provided text of the research paper, and the main body does not explicitly list software dependencies with version numbers. |
| Experiment Setup | No | The paper's NeurIPS checklist claims that training and test details, including hyperparameters and optimizer, can be found on 'page A.5'. However, 'page A.5' is not included in the provided text of the research paper, and the main body does not explicitly detail the experimental setup, such as hyperparameters or optimizer settings. |