Object Relational Graph with Teacher-Recommended Learning for Video Captioning | |
Zhang,Ziqi5,7; Shi,Yaya6; Yuan,Chunfeng7; Li,Bing1,2,7; Wang,Peijin3,5; Hu,Weiming4,5,7; Zha,Zhengjun6 | |
2020-06 | |
会议日期 | 2020.6.14-19 |
会议地点 | 线上 |
页码 | 13278-13288 |
英文摘要 | Taking full advantage of the information from both vision and language is critical for the video captioning task. Existing models lack adequate visual representation due to the neglect of interaction between object, and sufficient training for content-related words due to long-tailed problems. In this paper, we propose a complete video captioning system including both a novel model and an effective training strategy. Specifically, we propose an object relational graph (ORG) based encoder, which captures more detailed interaction features to enrich visual representation. Meanwhile, we design a teacher-recommended learning (TRL) method to make full use of the successful external language model (ELM) to integrate the abundant linguistic knowledge into the caption model. The ELM generates more semantically similar word proposals which extend the groundtruth words used for training to deal with the long-tailed problem. Experimental evaluations on three benchmarks: MSVD, MSR-VTT and VATEX show the proposed ORGTRL system achieves state-of-the-art performance. Extensive ablation studies and visualizations illustrate the effectiveness of our system. |
会议录 | 2020 |
内容类型 | 会议论文 |
源URL | [http://ir.ia.ac.cn/handle/173211/48752] |
专题 | 自动化研究所_模式识别国家重点实验室_视频内容安全团队 |
通讯作者 | Yuan,Chunfeng |
作者单位 | 1.State Key Laboratory of Communication Content Cognition, People's Daily Online 2.PeopleAI, Inc. 3.Aerospace Information Research Institute, CAS 4.Center for Excellence in Brain Science and Intelligence Technology, CAS 5.University of Chinese Academy of Sciences 6.University of Science and Technology of China 7.National Laboratory of Pattern Recognition, CASIA |
推荐引用方式 GB/T 7714 | Zhang,Ziqi,Shi,Yaya,Yuan,Chunfeng,et al. Object Relational Graph with Teacher-Recommended Learning for Video Captioning[C]. 见:. 线上. 2020.6.14-19. |
个性服务 |
查看访问统计 |
相关权益政策 |
暂无数据 |
收藏/分享 |
除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。
修改评论