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..
G2RL: Geometry-Guided Representation Learning for Facial Action Unit Intensity Estimation
Authors: Yingruo Fan, Zhaojiang Lin
IJCAI 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The experimental results on two benchmark datasets demonstrate that our method is comparable with the state-of-the-art approaches, and validate the effectiveness of incorporating external geometric knowledge for facial AU intensity estimation. 4 Experiments |
| Researcher Affiliation | Academia | 1Department of Electrical and Electronic Engineering, University of Hong Kong 2Department of Electronic and Computer Engineering, Hong Kong University of Science and Technology EMAIL, EMAIL |
| Pseudocode | No | The paper describes the methodology using text and mathematical equations, but does not include any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide an explicit statement or a link to open-source code for the described methodology. It mentions using Dlib and TensorFlow but does not offer its own implementation code. |
| Open Datasets | Yes | We adopt two benchmark datasets, BP4D [Zhang et al., 2014] and DISFA [Mavadati et al., 2013], for our experiments. |
| Dataset Splits | Yes | In our experiments, we evaluate our method on BP4D using the official training/development partitions. While for DISFA, the 3-fold subject independent cross-validation is adopted for evaluation. |
| Hardware Specification | Yes | The framework is implemented in Tensorflow2 and NVIDIA Ge Force GTX 1080Ti GPUs are used. |
| Software Dependencies | Yes | The framework is implemented in Tensorflow2 |
| Experiment Setup | Yes | In the training phase, we use the Adam optimizer [Kingma and Ba, 2014], with the base learning rate of 5e-4. For parameter setting, we set the value of the standard deviation σ to 2 in the heatmap ground-truth generation (Equation 1), and assign 0.05 to λ in the overall loss function (Equation 8) according to the performance of G2RL. |