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..
ReFIR: Grounding Large Restoration Models with Retrieval Augmentation
Authors: Hang Guo, Tao Dai, Zhihao Ouyang, Taolin Zhang, Yaohua Zha, Bin Chen, Shu-Tao Xia
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments demonstrate that Re FIR can achieve not only high-fidelity but also realistic restoration results. |
| Researcher Affiliation | Collaboration | Hang Guo1 Tao Dai 2 Zhihao Ouyang3 Taolin Zhang1 Yaohua Zha1 Bin Chen4 Shu-tao Xia1,5 1Tsinghua University 2Shenzhen University 3Aitist.ai 4Harbin Institute of Technology 5Peng Cheng Laboratory |
| Pseudocode | No | The paper does not contain any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | Yes | https://github.com/csguoh/Re FIR |
| Open Datasets | Yes | The datasets for this setting employ the widely used Ref SR dataset including CUFED5 [56, 57] and WR-SR [47]... And we use DIV2K [58] as the high-quality image database for retrieval... |
| Dataset Splits | No | The paper uses standard datasets like CUFED5 and WR-SR and mentions Real Photo60 for evaluation, but does not explicitly provide training/validation/test dataset splits, percentages, or methodologies for how these splits were created or used for their specific experiments. |
| Hardware Specification | Yes | We use an input image with the resolution of 2048 × 2048 to evaluate the GPU memory and the inference time on one single 80G NVIDIA A100 GPU. |
| Software Dependencies | No | The paper does not explicitly provide specific software dependencies with version numbers. |
| Experiment Setup | Yes | For a fair comparison, we use one reference image if not specified... the ILQ is up-sampled to the desired size using Bicubic... We use reflective padding... We use fixed random seeds... The hyperparameters of different baselines follow their original settings... In practice, we adopt a moderate s = 0.5 to trade off the hallucination and the overuse of the reference image. |