PDF] LoL-V2T: Large-Scale Esports Video Description Dataset
Descrição
The dataset, which the authors call LoL-V2T, is the largest video description dataset in the video game domain, and includes 9,723 clips with 62,677 captions, and the masking can significantly improve performance. Esports is a fastest-growing new field with a largely online-presence, and is creating a demand for automatic domain-specific captioning tools. However, at the current time, there are few approaches that tackle the esports video description problem. In this work, we propose a large-scale dataset for esports video description, focusing on the popular game "League of Legends". The dataset, which we call LoL-V2T, is the largest video description dataset in the video game domain, and includes 9,723 clips with 62,677 captions. This new dataset presents multiple new video captioning challenges such as large amounts of domain-specific vocabulary, subtle motions with large importance, and a temporal gap between most captions and the events that occurred. In order to tackle the issue of vocabulary, we propose a masking the domain-specific words and provide additional annotations for this. In our results, we show that the dataset poses a challenge to existing video captioning approaches, and the masking can significantly improve performance. Our dataset and code is publicly available1.
PDF] MSR-VTT: A Large Video Description Dataset for Bridging Video
Edgar Simo-Serra's research works
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Edgar Simo-Serra's research works
PDF] MSR-VTT: A Large Video Description Dataset for Bridging Video
LoL-V2T: Large-Scale Esports Video Description Dataset
PDF) CS-lol: a Dataset of Viewer Comment with Scene in E-sports
PDF] LoL-V2T: Large-Scale Esports Video Description Dataset
PDF] MSR-VTT: A Large Video Description Dataset for Bridging Video
Edgar Simo-Serra's research works
Edgar Simo-Serra's research works
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