G2RL: Geometry-Guided Representation Learning for Facial Action Unit Intensity Estimation
Authors: Yingruo Fan, Zhaojiang Lin
IJCAI 2020 | Conference PDF | Archive PDF | Plain Text | 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 yingruo@hku.hk, zlinao@connect.ust.hk |
| 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. |