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..
Incomplete Multimodality-Diffused Emotion Recognition
Authors: Yuanzhi Wang, Yong Li, Zhen Cui
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We perform extensive experiments on publicly available MER datasets and achieve superior or comparable results across different missing modality patterns. |
| Researcher Affiliation | Academia | Yuanzhi Wang, Yong Li, Zhen Cui PCA Lab, Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education, School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China. EMAIL |
| Pseudocode | No | The paper describes the method using equations and text but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Codes are released at https://github.com/mdswyz/IMDer. |
| Open Datasets | Yes | We consider two standard MER datasets to conduct experiments, including CMU-MOSI [32] and CMU-MOSEI [33]. |
| Dataset Splits | Yes | CMU-MOSI consists of 2199 monologue video clips. Where 1284, 229, and 686 samples are used as training, validation, and testing set. CMU-MOSEI contains 22856 samples of movie review video clips. Where 16326 samples are used for training, the remaining 1871 and 4659 samples are used for validation and testing. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper mentions software tools like BERT, Facet, and COVAREP but does not specify their version numbers. |
| Experiment Setup | Yes | The optimal setting for β is set to 0.1 via the performance on the validation set. |