TY - JOUR AU - Xiao, Shuwen AU - Zhao, Zhou AU - Zhang, Zijian AU - Yan, Xiaohui AU - Yang, Min PY - 2020/04/03 Y2 - 2024/03/28 TI - Convolutional Hierarchical Attention Network for Query-Focused Video Summarization JF - Proceedings of the AAAI Conference on Artificial Intelligence JA - AAAI VL - 34 IS - 07 SE - AAAI Technical Track: Vision DO - 10.1609/aaai.v34i07.6929 UR - https://ojs.aaai.org/index.php/AAAI/article/view/6929 SP - 12426-12433 AB - <p>Previous approaches for video summarization mainly concentrate on finding the most diverse and representative visual contents as video summary without considering the user's preference. This paper addresses the task of query-focused video summarization, which takes user's query and a long video as inputs and aims to generate a query-focused video summary. In this paper, we consider the task as a problem of computing similarity between video shots and query. To this end, we propose a method, named Convolutional Hierarchical Attention Network (CHAN), which consists of two parts: feature encoding network and query-relevance computing module. In the encoding network, we employ a convolutional network with local self-attention mechanism and query-aware global attention mechanism to learns visual information of each shot. The encoded features will be sent to query-relevance computing module to generate query-focused video summary. Extensive experiments on the benchmark dataset demonstrate the competitive performance and show the effectiveness of our approach.</p> ER -