Gaussian-Based Instance-Adaptive Intensity Modeling for Point-Supervised Facial Expression Spotting
Authors: Yicheng Deng, Hideaki Hayashi, Hajime Nagahara
ICLR 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on the SAMM-LV and CAS(ME)2 datasets demonstrate the effectiveness of our proposed framework. Code is available at https://github.com/KinopioIsAllIn/GIM. |
| Researcher Affiliation | Academia | Yicheng Deng, Hideaki Hayashi, Hajime Nagahara Osaka University EMAIL, EMAIL |
| Pseudocode | No | The paper describes the steps for constructing Gaussian distributions (Step 1, Step 2, Step 3) but does not present them in a formal pseudocode or algorithm block. |
| Open Source Code | Yes | Code is available at https://github.com/KinopioIsAllIn/GIM. |
| Open Datasets | Yes | We follow the protocol of MEGC2021 and validate our method on two datasets: SAMM-LV (Yap et al., 2020) and CAS(ME)2 (Qu et al., 2017). |
| Dataset Splits | Yes | We employ a leave-one-subject-out cross-validation strategy in the experiments. |
| Hardware Specification | No | The paper does not explicitly describe the hardware used for experiments, such as specific GPU or CPU models. |
| Software Dependencies | No | The model is trained by the Adam optimizer (Kingma, 2015) on both datasets for 100 epochs with a learning rate of 2.0 10 5 and a weight decay of 0.1. This mentions the Adam optimizer but does not specify software versions for libraries or frameworks like PyTorch or TensorFlow. |
| Experiment Setup | Yes | The model is trained by the Adam optimizer (Kingma, 2015) on both datasets for 100 epochs with a learning rate of 2.0 10 5 and a weight decay of 0.1. The coefficient δ for duration estimation is set to 1.2. For the multi-stage training, the epochs for each stage are 1, 4, and 95, respectively. kc is set to 16 for ME and 32 for Ma E, respectively. ks1 is set to 3 for MEs and 5 for Ma Es, ks2 is set to 2 for MEs and 4 for Ma Es. We set the loss weight λ to 0.1, 0.3, and 2.0 10 5 for SAMM-LV, and to 0.1, 2.5, and 1.4 10 4 for CAS(ME)2, respectively. The threshold θ for estimating the rough duration of each expression proposal decreases linearly from 0.8 to 0.5 over 30 epochs and then remains at 0.5 until the end. |