Dense-Captioning Events in Videos: SYSU Submission to ActivityNet Challenge 2020
School of Data and Computer Science, Sun Yat-sen University（中山大学数据与计算机学院）；Key Laboratory of Machine Intelligence and Advanced Computing, Ministry of Education（教育部机器智能与先进计算重点实验室）；Guangdong Province Key Laboratory of Information Security Technology（广东省信息安全技术重点实验室）
英文摘要：
This technical report presents a brief description of our submission to the dense video captioning task of ActivityNet Challenge 2020. Our approach follows a two-stage pipeline: first, we extract a set of temporal event proposals; then we propose a multi-event captioning model to capture the event-level temporal relationships and effectively fuse the multi-modal information. Our approach achieves a 9.28 METEOR score on the test set.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
