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 [1].
GSDNet: Revisiting Incomplete Multimodality-Diffusion Emotion Recognition from the Perspective of Graph Spectrum
Authors: Yuntao Shou, Jun Yao, Tao Meng, Wei Ai, Cen Chen, Keqin Li
IJCAI 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct extensive experiments on two MERC datasets to conduct experiments, including CMU-MOSI [Zadeh et al., 2016], and CMU-MOSEI [Zadeh et al., 2018]. ... Extensive experiments have demonstrated that GSDNet achieves state-of-the-art emotion recognition performance in various modality loss scenarios. |
| Researcher Affiliation | Academia | 1College of Computer and Mathematics, Central South University of Forestry and Technology, Changsha, Hunan 410004, China 2Department of Computer Science, Anhui Normal University, Anhui 24100, China 3Future Technology Institute, South China University of Technology, Guangdong 510641, China 4Department of Computer Science, State University of New York, New Paltz, New York 12561, USA EMAIL, EMAIL, EMAIL, EMAIL, EMAIL, EMAIL |
| Pseudocode | No | The paper describes the proposed method (GSDNet) using mathematical equations and a block diagram (Figure 2), but it does not contain a clearly labeled 'Pseudocode' or 'Algorithm' block. |
| Open Source Code | No | The paper does not contain any explicit statement regarding the release of source code, nor does it provide a link to a code repository. |
| Open Datasets | Yes | We conduct extensive experiments on two MERC datasets to conduct experiments, including CMU-MOSI [Zadeh et al., 2016], and CMU-MOSEI [Zadeh et al., 2018]. |
| Dataset Splits | No | The paper mentions using CMU-MOSI and CMU-MOSEI datasets but does not explicitly provide details about training, validation, or test dataset splits within its text. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, processor types) used for running its experiments. |
| Software Dependencies | No | The paper mentions using pre-trained models like RoBERTa-Large, DenseNet, and wav2vec for feature extraction, but it does not specify the software environment, programming languages, libraries, or their specific version numbers used for implementing and running the GSDNet model. |
| Experiment Setup | No | The paper mentions that 'β is a hyperparameter' in the total loss function (Equation 20), but it does not provide its concrete value or other specific experimental setup details such as learning rate, batch size, number of epochs, or optimizer settings. |