用于视频重定位的邻接矩阵的生成
Generating Adjacency Matrix for Video Relocalization
摘要：
在本文中，我们继续我们的工作，视频重定位任务。在利用图卷积提取视频帧内和帧间特征的基础上，改进了基于相似度度量的图卷积方法，其加权邻接矩阵是通过计算图中任意两个不同时间步的特征之间的相似度度量得到的。在ActivityNet v1.2和Thumos14数据集上的实验表明了该改进的有效性，其性能优于现有的方法。
英文摘要：
In this paper, we continue our work on video relocalization task. Based on using graph convolution to extract intra-video and inter-video frame features, we improve the method by using similarity-metric based graph convolution, whose weighted adjacency matrix is achieved by calculating similarity metric between features of any two different time steps in the graph. Experiments on ActivityNet v1.2 and Thumos14 dataset show the effectiveness of this improvement, and it outperforms the state-of-the-art methods.
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。
