SAIVT-BNEWS: An Australian Broadcast News Video Dataset for Entity Extraction, and More

David Dean
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Abstract

Recently QUT have released a set of annotated broadcast news videos (SAIVT-BNEWS) that we have made available at our website (https://www.qut.edu.au/research/saivt). This presentation will outline the dataset itself, covering 50 or so short news clips surrounding a single political event with many entities appearing in multuple records, and cover interesting research that QUT has, is currently, and is interested in performing on this dataset in the future. This presentation will cover existing published research, including image processing tasks like face detection and clustering; and speech processing tasks (including the use of visual speech) like speech detection, speaker recognition, and speaker diarisation. We have also started very interesting research on fusing multiple sources of information, including metadata, OCR, faces, speech, and scene detection to improve the performance of many techniques, but with a focus on improving the automatic extraction of entities (people, places, companies and organisations) from large volumes of audio-visual data, and this will also be addressed in this talk. As this dataset is publicly available for free to all researchers, QUT hopes that other researchers will make use of, and improve upon this dataset as well.
用于实体提取的澳大利亚广播新闻视频数据集,以及更多
最近,昆士兰科技大学发布了一组带注释的广播新闻视频(SAIVT-BNEWS),并在我们的网站(https://www.qut.edu.au/research/saivt)上提供。本演讲将概述数据集本身,涵盖围绕单个政治事件的50个左右的短新闻片段,其中多个记录中出现了许多实体,并涵盖QUT已经,目前和将来有兴趣在该数据集上执行的有趣研究。本演讲将涵盖现有的已发表的研究,包括图像处理任务,如人脸检测和聚类;以及语音处理任务(包括使用视觉语音),如语音检测、说话人识别和说话人日记。我们也开始了非常有趣的研究,融合多种信息来源,包括元数据、OCR、人脸、语音和场景检测,以提高许多技术的性能,但重点是提高从大量视听数据中自动提取实体(人、地点、公司和组织)的能力,这也将在本次演讲中讨论。由于此数据集对所有研究人员免费公开,QUT希望其他研究人员也能使用并改进此数据集。
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