Multimodal LLM-based Query Paraphrasing for Video Search

Abstract

Text-to-video retrieval answers user queries through searches based on concepts and embeddings. However, due to limitations in the size of the concept bank and the amount of training data, answering queries in the wild is not always effective because of the out-of-vocabulary problem. Furthermore, neither concept-based nor embedding-based search can perform reasoning to consolidate search results for complex queries that include logical and spatial constraints. To address these challenges, we leverage large language models (LLMs) to paraphrase queries using text-to-text (T2T), text-to-image (T2I), and image-to-text (I2T) transformations. These transformations rephrase abstract concepts into simpler terms to mitigate the out-of-vocabulary problem. Additionally, complex relationships within a query can be decomposed into simpler sub-queries, improving retrieval performance by effectively fusing the search results of these sub-queries. To mitigate the issue of LLM hallucination, this paper also proposes a novel consistency-based verification strategy to filter out factually incorrect paraphrased queries. 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 effectively resolved through query paraphrasing.