{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,5]],"date-time":"2026-06-05T03:24:04Z","timestamp":1780629844177,"version":"3.54.1"},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019,8]]},"abstract":"<jats:p>Open-ended video question answering aims to automatically generate the natural-language answer from referenced video contents according to the given question. Currently, most existing approaches focus on short-form video question answering with multi-modal recurrent encoder-decoder networks. Although these works have achieved promising performance, they may still be ineffectively applied to long-form video question answering due to the lack of long-range dependency modeling and the suffering from the heavy computational cost. To tackle these problems, we propose a fast hierarchical convolutional self-attention encoder-decoder network. Concretely, we first develop a hierarchical convolutional self-attention encoder to efficiently model long-form video contents, which builds the hierarchical structure for video sequences and captures question-aware long-range dependencies from video context. We then devise a multi-scale attentive decoder to incorporate multi-layer video representations for answer generation, which avoids the information missing of the top encoder layer. The extensive experiments show the effectiveness and efficiency of our method.<\/jats:p>","DOI":"10.24963\/ijcai.2019\/609","type":"proceedings-article","created":{"date-parts":[[2019,7,28]],"date-time":"2019-07-28T07:46:05Z","timestamp":1564299965000},"page":"4383-4389","source":"Crossref","is-referenced-by-count":14,"title":["Open-Ended Long-Form Video Question Answering via Hierarchical Convolutional Self-Attention Networks"],"prefix":"10.24963","author":[{"given":"Zhu","family":"Zhang","sequence":"first","affiliation":[{"name":"College of Computer Science, Zhejiang University, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhou","family":"Zhao","sequence":"additional","affiliation":[{"name":"College of Computer Science, Zhejiang University, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhijie","family":"Lin","sequence":"additional","affiliation":[{"name":"College of Computer Science, Zhejiang University, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jingkuan","family":"Song","sequence":"additional","affiliation":[{"name":"University of Electronic Science and Technology of China, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaofei","family":"He","sequence":"additional","affiliation":[{"name":"State Key Lab of CAD&CG, Zhejiang University, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"10584","event":{"name":"Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}","theme":"Artificial Intelligence","location":"Macao, China","acronym":"IJCAI-2019","number":"28","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"start":{"date-parts":[[2019,8,10]]},"end":{"date-parts":[[2019,8,16]]}},"container-title":["Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2019,7,28]],"date-time":"2019-07-28T07:50:27Z","timestamp":1564300227000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2019\/609"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2019,8]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2019\/609","relation":{},"subject":[],"published":{"date-parts":[[2019,8]]}}}