Generating Adjacency Matrix for Video Relocalization
This incremental improvement addresses video analysis tasks for researchers and practitioners.
The paper tackles video relocalization by improving graph convolution with a similarity-metric based approach that uses weighted adjacency matrices from feature similarities, achieving state-of-the-art results on ActivityNet v1.2 and Thumos14 datasets.
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.