  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.
