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..
Learning Motion-Robust Remote Photoplethysmography through Arbitrary Resolution Videos
Authors: Jianwei Li, Zitong Yu, Jingang Shi
AAAI 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on three benchmark datasets (UBFC-r PPG, COHFACE and PURE) demonstrate the superior performance of the proposed method. |
| Researcher Affiliation | Academia | Jianwei Li1*, Zitong Yu2*, Jingang Shi1 1School of Software Engineering,Xi an Jiaotong University 2Great Bay University |
| Pseudocode | No | The paper describes the model architecture and its components (PFE, TFA) with figures and mathematical formulations, but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | The codes are available at https://github.com/LJWGIT/Arbitrary Resolution r PPG. |
| Open Datasets | Yes | Extensive experiments on three benchmark datasets (UBFC-r PPG, COHFACE and PURE) demonstrate the superior performance of the proposed method. |
| Dataset Splits | Yes | We first conduct experiments of r PPG-based HR measurement on three benchmark datasets with their original protocols and normal setting. |
| Hardware Specification | Yes | The proposed method is trained with batchsize 2 on RTX3090 GPU with Py Torch. ... Considering the deployment on edge devices, we provide a detailed analysis of complexity as well as the inference times on different devices (i.e., Nvidia RTX 3090 and Jetson AGX Orin) in Table 4. |
| Software Dependencies | No | The paper mentions 'Py Torch' but does not specify its version number or any other software dependencies with version numbers. |
| Experiment Setup | Yes | The proposed method is trained with batchsize 2 on RTX3090 GPU with Py Torch. The Adam optimizer is used and the learning rate is set as 1e-4. The weight decay is 5e-5. |