  In this paper, we focus on video relocalization task, which uses a query
video clip as input to retrieve a semantic relative video clip in another
untrimmed long video. we find that in video relocalization datasets, there
exists a phenomenon showing that there does not exist consistent relationship
between feature similarity by frame and feature similarity by video, which
affects the feature fusion among frames. However, existing video relocalization
methods do not fully consider it. Taking this phenomenon into account, in this
article, we treat video features as a graph by concatenating the query video
feature and proposal video feature along time dimension, where each timestep is
treated as a node, each row of the feature matrix is treated as feature of each
node. Then, with the power of graph neural networks, we propose a Multi-Graph
Feature Fusion Module to fuse the relation feature of this graph. After
evaluating our method on ActivityNet v1.2 dataset and Thumos14 dataset, we find
that our proposed method outperforms the state of art methods.
