Knowledge Guided Entity-aware Video Captioning and A Basketball Benchmark
This work addresses the domain-specific challenge of enhancing video captioning for sports applications, such as basketball broadcasting, by integrating external knowledge, but it is incremental as it builds on existing encoder-decoder frameworks.
The paper tackles the problem of generating video captions with specific entity names and fine-grained actions, particularly for basketball live text broadcast, by proposing a new multimodal knowledge graph benchmark and a network that outperforms advanced models on multiple sports benchmarks.
Despite the recent emergence of video captioning models, how to generate the text description with specific entity names and fine-grained actions is far from being solved, which however has great applications such as basketball live text broadcast. In this paper, a new multimodal knowledge graph supported basketball benchmark for video captioning is proposed. Specifically, we construct a multimodal basketball game knowledge graph (KG_NBA_2022) to provide additional knowledge beyond videos. Then, a multimodal basketball game video captioning (VC_NBA_2022) dataset that contains 9 types of fine-grained shooting events and 286 players' knowledge (i.e., images and names) is constructed based on KG_NBA_2022. We develop a knowledge guided entity-aware video captioning network (KEANet) based on a candidate player list in encoder-decoder form for basketball live text broadcast. The temporal contextual information in video is encoded by introducing the bi-directional GRU (Bi-GRU) module. And the entity-aware module is designed to model the relationships among the players and highlight the key players. Extensive experiments on multiple sports benchmarks demonstrate that KEANet effectively leverages extera knowledge and outperforms advanced video captioning models. The proposed dataset and corresponding codes will be publicly available soon