Interpretable Tensor Fusion
Authors: Saurabh Varshneya, Antoine Ledent, Philipp Liznerski, Andriy Balinskyy, Purvanshi Mehta, Waleed Mustafa, Marius Kloft
IJCAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on six realworld datasets show that In Tense outperforms existing state-of-the-art multimodal interpretable approaches in terms of accuracy and interpretability. |
| Researcher Affiliation | Collaboration | Saurabh Varshneya1 , Antoine Ledent2 , Philipp Liznerski1 , Andriy Balinskyy1 , Purvanshi Mehta3 , Waleed Mustafa1 and Marius Kloft1 1RPTU Kaiserslautern-Landau 2Singapore Management University 3Lica World |
| Pseudocode | No | The paper does not contain any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | Full paper with technical appendix and code is available at: https://arxiv.org/abs/2405.04671 |
| Open Datasets | Yes | To evaluate In Tense in sentiment analysis, we employed CMU-MOSEI [Bagher Zadeh et al., 2018], the largest dataset of sentence-level sentiment analysis for real-world online videos, and CMU-MOSI [Zadeh et al., 2016], a collection of annotated opinion video clips... To assess our approach s effectiveness in these tasks, we utilized UR-FUNNY [Hasan et al., 2019] for humor detection and MUSt ARD [Castro et al., 2019] for sarcasm detection... For this paper, we considered the ENRICO [Leiva et al., 2020] dataset as an example for layout design categorization. We also include results for Audiovision-MNIST (AV-MNIST) [Vielzeuf et al., 2018], a multimodal dataset comprising images of handwritten and recordings of spoken digits. |
| Dataset Splits | Yes | In order to compare performance and ensure reproducibility, we followed the experimental setup (e.g., data preprocessing, encodings of different modalities) of the Multi Bench [Liang et al., 2021] benchmark for all the experiments. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running experiments (e.g., GPU/CPU models, memory specifications). |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., 'Python 3.8, PyTorch 1.9'). |
| Experiment Setup | No | The paper states that it 'followed the experimental setup (e.g., data preprocessing, encodings of different modalities) of the Multi Bench [Liang et al., 2021] benchmark', but it does not explicitly list concrete hyperparameter values or detailed training configurations within its own text. |