  Visual Query Localization on long-form egocentric videos requires
spatio-temporal search and localization of visually specified objects and is
vital to build episodic memory systems. Prior work develops complex multi-stage
pipelines that leverage well-established object detection and tracking methods
to perform VQL. However, each stage is independently trained and the complexity
of the pipeline results in slow inference speeds. We propose VQLoC, a novel
single-stage VQL framework that is end-to-end trainable. Our key idea is to
first build a holistic understanding of the query-video relationship and then
perform spatio-temporal localization in a single shot manner. Specifically, we
establish the query-video relationship by jointly considering query-to-frame
correspondences between the query and each video frame and frame-to-frame
correspondences between nearby video frames. Our experiments demonstrate that
our approach outperforms prior VQL methods by 20% accuracy while obtaining a
10x improvement in inference speed. VQLoC is also the top entry on the Ego4D
VQ2D challenge leaderboard. Project page: https://hwjiang1510.github.io/VQLoC/
