  We introduce EQUI-VOCAL: a new system that automatically synthesizes queries
over videos from limited user interactions. The user only provides a handful of
positive and negative examples of what they are looking for. EQUI-VOCAL
utilizes these initial examples and additional ones collected through active
learning to efficiently synthesize complex user queries. Our approach enables
users to find events without database expertise, with limited labeling effort,
and without declarative specifications or sketches. Core to EQUI-VOCAL's design
is the use of spatio-temporal scene graphs in its data model and query language
and a novel query synthesis approach that works on large and noisy video data.
Our system outperforms two baseline systems -- in terms of F1 score, synthesis
time, and robustness to noise -- and can flexibly synthesize complex queries
that the baselines do not support.
