  To enhance research on multimodal knowledge base and multimodal information
processing, we propose a new task called multimodal entity tagging (MET) with a
multimodal knowledge base (MKB). We also develop a dataset for the problem
using an existing MKB. In an MKB, there are entities and their associated texts
and images. In MET, given a text-image pair, one uses the information in the
MKB to automatically identify the related entity in the text-image pair. We
solve the task by using the information retrieval paradigm and implement
several baselines using state-of-the-art methods in NLP and CV. We conduct
extensive experiments and make analyses on the experimental results. The
results show that the task is challenging, but current technologies can achieve
relatively high performance. We will release the dataset, code, and models for
future research.
