Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Micro-Expression Recognition Enhanced by Macro-Expression from Spatial-Temporal Domain
Authors: Bin Xia, Shangfei Wang
IJCAI 2021 | Venue PDF | LLM Run Details | Input Tokens: 17,014 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,990 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on three benchmark databases demonstrate the superiority of the proposed method. |
| Researcher Affiliation | Academia | Bin Xia1 and Shangfei Wang1,2 1Key Lab of Computing and Communication Software of Anhui Province, School of Computer Science and Technology, University of Science and Technology of China 2Anhui Robot Technology Standard Innovation Base EMAIL, EMAIL |
| Pseudocode | No | The paper does not contain any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement about releasing source code or a link to a code repository for the described methodology. |
| Open Datasets | Yes | There are three databases commonly used for MER, i.e., CASME II [Yan et al., 2014], SMIC [Li et al., 2013] and SAMM [Davison et al., 2016]. Two popular lab-collected databases, i.e., CK+[Lucey et al., 2010] and MMI[Pantic et al., 2005] are adopted as macro-expression. |
| Dataset Splits | Yes | Leave-one-subject-out (LOSO) cross-validation is used in all experiments. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running the experiments. |
| Software Dependencies | No | The paper mentions using ResNet18 and Adam optimizer but does not specify version numbers for any software dependencies or libraries. |
| Experiment Setup | Yes | We set the sampling interval as s = 2k(k = 0, 1, 2, , sc 1). We use Eq.8 to update the parameter of Mi Net with the Adam optimizer, and set λ1 = λ4 = 0.1, λ2 = λ3 = 0.2. |