Graph Based Temporal Aggregation for Video Retrieval

Graph Based Temporal Aggregation for Video Retrieval

Large scale video retrieval is a field of study with a lot of ongoing research. Most of the work in the field is on video retrieval through text queries using techniques such as VSE++.However, there is little research done on video retrieval through image queries, and the work that has been done in this field either uses image queries from within the video dataset or iterates through videos frame by frame. These approaches are not generalized for queries from outside the dataset and do not scale well for large video datasets. To overcome these issues, we propose a new approach for video retrieval through image queries where an undirected graph is constructed from the combined set of frames from all videos to be searched. The node features of this graph are used in the task of video retrieval. Experimentation is done on the MSR-VTT dataset by using query images from outside the dataset. To evaluate this novel approach [email protected], [email protected] and [email protected] metrics are calculated. Two different ResNet models namely, ResNet-152 and ResNet-50 are used in this study.

基于图的时间聚合用于视频检索

大规模视频检索是一个研究领域，正在进行大量研究。该领域的大部分工作是使用VSE ++等技术通过文本查询检索视频。.. 但是，关于通过图像查询进行视频检索的研究很少，并且在该领域已完成的工作要么使用来自视频数据集的图像查询，要么逐帧迭代视频。这些方法未针对来自数据集外部的查询进行通用化，并且不适用于大型视频数据集。为了克服这些问题，我们提出了一种通过图像查询进行视频检索的新方法，其中从要搜索的所有视频的组合帧集中构造了无向图。该图的节点特征用于视频检索任务。通过使用来自数据集外部的查询图像，对MSR-VTT数据集进行了实验。为了评估这种新颖的方法[受电子邮件保护]，[受电子邮件保护]和计算[受电子邮件保护]指标。本研究使用两种不同的ResNet模型，即ResNet-152和ResNet-50。 （阅读更多）