  Text-to-video retrieval answers user queries through search by concepts and
embeddings. Limited by the size of the concept bank and the amount of training
data, answering queries in the wild is not always effective due to the
out-of-vocabulary problem. Furthermore, neither concept-based nor
embedding-based search can perform reasoning to consolidate the search results
for complex queries mixed with logical and spatial constraints. To address
these problems, we leverage large language models (LLM) to paraphrase the query
by text-to-text (T2T), text-to-image (T2I), and image-to-text (I2T)
transformations. These transformations rephrase abstract concepts into simple
words to address the out-of-vocabulary problem. Furthermore, the complex
relationship in a query can be decoupled into simpler sub-queries, yielding
better retrieval performance when fusing the search results of these
sub-queries. To address the LLM hallucination problem, this paper also proposes
a novel consistency-based verification strategy to filter the paraphrased
queries that are factually incorrect. Extensive experiments are conducted for
ad-hoc video search and known-item search on the TRECVid datasets. We provide
empirical insights into how traditionally difficult-to-answer queries can be
resolved by query paraphrasing.
