  Advances in ML have motivated the design of video analytics systems that
allow for structured queries over video datasets. However, existing systems
limit query expressivity, require users to specify an ML model per predicate,
rely on complex optimizations that trade off accuracy for performance, and
return large amounts of redundant and low-quality results. This paper focuses
on the recently developed Vision-Language Models (VLMs) that allow users to
query images using natural language like "cars during daytime at traffic
intersections." Through an in-depth analysis, we show VLMs address three
limitations of current video analytics systems: general expressivity, a single
general purpose model to query many predicates, and are both simple and fast.
However, VLMs still return large numbers of redundant and low-quality results
that can overwhelm and burden users. In addition, VLMs often require manual
prompt engineering to improve result relevance.
  We present Zelda: a video analytics system that uses VLMs to return both
relevant and semantically diverse results for top-K queries on large video
datasets. Zelda prompts the VLM with the user's query in natural language.
Zelda then automatically adds discriminator and synonym terms to boost
accuracy, and terms to identify low-quality frames. To improve result
diversity, Zelda uses semantic-rich VLM embeddings in an algorithm that prunes
similar frames while considering their relevance to the query and the number of
top-K results requested. We evaluate Zelda across five datasets and 19 queries
and quantitatively show it achieves higher mean average precision (up to 1.15x)
and improves average pairwise similarity (up to 1.16x) compared to using VLMs
out-of-the-box. We also compare Zelda to a state-of-the-art video analytics
engine and show that Zelda retrieves results 7.5x (up to 10.4x) faster for the
same accuracy and frame diversity.
