CASIA OpenIR  > 智能感知与计算
Language-driven Temporal Activity Localization: A Semantic Matching Reinforcement Learning Model
Wang, Weining; Huang, Yan; Wang, Liang
2019-06
会议名称Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition
会议日期2019-6-16
会议地点美国长滩
摘要

Current studies on action detection in untrimmed videos are mostly designed for action classes, where an action is described at word level such as jumping, tumbling, swing, etc. This paper focuses on a rarely investigated problem of localizing an activity via a sentence query which would be more challenging and practical. Considering that current methods are generally time-consuming due to the dense frame-processing manner, we propose a recurrent neural network based reinforcement learning model which selectively observes a sequence of frames and associates the given sentence with video content in a matching-based manner. However, directly matching sentences with video content performs poorly due to the large visual-semantic discrepancy. Thus, we extend the method to a semantic matching reinforcement learning (SM-RL) model by extracting semantic concepts of videos and then fusing them with global context features. Extensive experiments on three benchmark datasets, TACoS, Charades-STA and DiDeMo, show that our method achieves the state-of-the-art performance with a high detection speed, demonstrating both effectiveness and efficiency of our method.

七大方向——子方向分类多模态智能
文献类型会议论文
条目标识符http://ir.ia.ac.cn/handle/173211/40382
专题智能感知与计算
作者单位中国科学院自动化研究所
第一作者单位中国科学院自动化研究所
推荐引用方式
GB/T 7714
Wang, Weining,Huang, Yan,Wang, Liang. Language-driven Temporal Activity Localization: A Semantic Matching Reinforcement Learning Model[C],2019.
条目包含的文件 下载所有文件
文件名称/大小 文献类型 版本类型 开放类型 使用许可
CVPR19-王卫宁.pdf(430KB)会议论文 开放获取CC BY-NC-SA浏览 下载
个性服务
推荐该条目
保存到收藏夹
查看访问统计
导出为Endnote文件
谷歌学术
谷歌学术中相似的文章
[Wang, Weining]的文章
[Huang, Yan]的文章
[Wang, Liang]的文章
百度学术
百度学术中相似的文章
[Wang, Weining]的文章
[Huang, Yan]的文章
[Wang, Liang]的文章
必应学术
必应学术中相似的文章
[Wang, Weining]的文章
[Huang, Yan]的文章
[Wang, Liang]的文章
相关权益政策
暂无数据
收藏/分享
文件名: CVPR19-王卫宁.pdf
格式: Adobe PDF
此文件暂不支持浏览
所有评论 (0)
暂无评论
 

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。