In recent days, streaming technology has greatly promoted the development in the field of livestream. Due to the excessive length of livestream records, it's quite essential to extract highlight segments with the aim of effective reproduction and redistribution. Although there are lots of approaches proven to be effective in the highlight detection for other modals, the challenges existing in livestream processing, such as the extreme durations, large topic shifts, much irrelevant information and so forth, heavily hamper the adaptation and compatibility of these methods. In this paper, we formulate a new task Livestream Highlight Detection, discuss and analyze the difficulties listed above and propose a novel architecture AntPivot to solve this problem. Concretely, we first encode the original data into multiple views and model their temporal relations to capture clues in a hierarchical attention mechanism. Afterwards, we try to convert the detection of highlight clips into the search for optimal decision sequences and use the fully integrated representations to predict the final results in a dynamic-programming mechanism. Furthermore, we construct a fully-annotated dataset AntHighlight to instantiate this task and evaluate the performance of our model. The extensive experiments indicate the effectiveness and validity of our proposed method.
翻译:在最近几天里,流流技术极大地促进了流流领域的发展。由于流传记录过长,为了有效复制和再分配的目的,提取亮点部分非常重要。虽然在突出探测其他模式方面有许多方法证明是有效的,但流传处理中存在的挑战,如极端持续时间、主题转移、许多无关的信息等等,严重妨碍了这些方法的适应和兼容性。在本文件中,我们制定了一个新的任务,即“流传高亮探测、讨论和分析上面列出的困难,并提出一个解决该问题的新结构 AntPivot。具体地说,我们首先将原始数据编码成多种观点,并模拟它们的时间关系,以便在一个分层注意机制中捕捉线索。随后,我们试图将光点的探测转换到寻找最佳决策序列的搜索中,并使用充分综合的表述方法来预测动态规划机制的最终结果。此外,我们建立了一个具有充分说明性的数据集 AntHistright光, 来描述这一任务,并评估我们模型的性能。广泛的实验表明我们拟议方法的有效性和有效性。