Graph Based Temporal Aggregation for Video Retrieval
This addresses the problem of scalable and generalized video retrieval for image queries from outside the dataset, which is an incremental improvement over existing methods.
The paper tackles video retrieval using image queries by constructing an undirected graph from video frames to improve generalization and scalability, achieving retrieval results measured by P@5, P@10, and P@20 metrics on the MSR-VTT dataset with ResNet models.
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 P@5, P@10 and P@20 metrics are calculated. Two different ResNet models namely, ResNet-152 and ResNet-50 are used in this study.