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Despite the recent emergence of video captioning models, how to generate thetext description with specific entity names and fine-grained actions is farfrom being solved, which however has great applications such as basketball livetext broadcast. In this paper, a new multimodal knowledge supported basketballbenchmark for video captioning is proposed. Specifically, we construct aMultimodal Basketball Game Knowledge Graph (MbgKG) to provide knowledge beyondvideos. Then, a Multimodal Basketball Game Video Captioning (MbgVC) datasetthat contains 9 types of fine-grained shooting events and 286 players'knowledge (i.e., images and names) is constructed based on MbgKG. We develop anovel framework in the encoder-decoder form named Entity-Aware Captioner (EAC)for basketball live text broadcast. The temporal information in video isencoded by introducing the bi-directional GRU (Bi-GRU) module. And themulti-head self-attention module is utilized to model the relationships amongthe players and select the key players. Besides, we propose a new performanceevaluation metric named Game Description Score (GDS), which measures not onlythe linguistic performance but also the accuracy of the names prediction.Extensive experiments on MbgVC dataset demonstrate that EAC effectivelyleverages external knowledge and outperforms advanced video captioning models.The proposed benchmark and corresponding codes will be publicly available soon.

Knowledge Graph Supported Benchmark and Video Captioning for Basketball