MuG: A Multimodal Classification Benchmark on Game Data with Tabular, Textual, and Visual Fields
This provides a new benchmark for researchers to evaluate multimodal models, but it is incremental as it builds on existing multimodal trends.
The authors introduced MuG, a multimodal classification benchmark with eight datasets from four game genres covering tabular, textual, and visual modalities, and conducted experiments showing its challenging and multimodal-dependent properties.
Previous research has demonstrated the advantages of integrating data from multiple sources over traditional unimodal data, leading to the emergence of numerous novel multimodal applications. We propose a multimodal classification benchmark MuG with eight datasets that allows researchers to evaluate and improve their models. These datasets are collected from four various genres of games that cover tabular, textual, and visual modalities. We conduct multi-aspect data analysis to provide insights into the benchmark, including label balance ratios, percentages of missing features, distributions of data within each modality, and the correlations between labels and input modalities. We further present experimental results obtained by several state-of-the-art unimodal classifiers and multimodal classifiers, which demonstrate the challenging and multimodal-dependent properties of the benchmark. MuG is released at https://github.com/lujiaying/MUG-Bench with the data, tutorials, and implemented baselines.