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- W3022778813 abstract "Sports video captioning refers to the task of automatically generating a textual description for sports events (football, basketball, or volleyball games). Although a great deal of previous work has shown promising performance in producing a coarse and a general description of a video but lack of professional sports knowledge, it is still quite challenging to caption a sports video with multiple fine-grained player's actions and complex group relationship between players. In this paper, we present a novel hierarchical recurrent neural network-based framework with an attention mechanism for sports video captioning, in which a motion representation module is proposed to capture individual pose attribute and dynamical trajectory cluster information with extra professional sports knowledge, and a group relationship module is employed to design a scene graph for modeling players' interaction by a gated graph convolutional network. Moreover, we introduce a new dataset called sports video captioning dataset-volleyball for evaluation. The proposed model is evaluated on three widely adopted public datasets and our collected new dataset, on which the effectiveness of our method is well demonstrated." @default.
- W3022778813 created "2020-05-13" @default.
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- W3022778813 date "2020-08-01" @default.
- W3022778813 modified "2023-10-14" @default.
- W3022778813 title "Sports Video Captioning via Attentive Motion Representation and Group Relationship Modeling" @default.
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- W3022778813 doi "https://doi.org/10.1109/tcsvt.2019.2921655" @default.
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