GEM: A General Evaluation Benchmark for Multimodal Tasks

Abstract

In this paper, we present GEM as a General Evaluation benchmark for Multimodal tasks. Different from existing datasets such as GLUE (Wang et al., 2018), SuperGLUE (Wang et al., 2019), XGLUE (Liang et al., 2020) and XTREME (Hu et al., 2020) that mainly focus on natural language tasks, GEM is a large-scale vision-language benchmark, which consists of GEM-I for image-language tasks and GEM-V for video-language tasks. Comparing with existing multimodal datasets such as MSCOCO (Chen et al., 2015) and Flicker30K (Vinyals et al., 2015) for image-language tasks, YouCook2 (Zhou et al., 2018) and MSR-VTT (Xu et al., 2016) for video-language tasks, GEM is not only the largest vision-language dataset covering image-language tasks and video-language tasks at the same time, but also labeled in multiple languages. We also provide two baseline models for this benchmark. We will release the dataset, code and baseline models, aiming to advance the development of multilingual multimodal research.