Q2E: Query-to-Event Decomposition for Zero-Shot Multilingual Text-to-Video Retrieval

Abstract

Recent approaches have shown impressive proficiency in extracting and leveraging parametric knowledge from Large-Language Models (LLMs) and Vision-Language Models (VLMs). In this work, we consider how we can improve the retrieval of videos related to complex real-world events by automatically extracting latent parametric knowledge about those events. We present Q2E: a Query-to-Event decomposition method for zero-shot multilingual text-to-video retrieval, adaptable across datasets, domains, LLMs, or VLMs. Our approach demonstrates that we can enhance the understanding of otherwise overly simplified human queries by decomposing the query using the knowledge embedded in LLMs and VLMs. We additionally show how to apply our approach to both visual and speech-based inputs. To combine this varied multimodal knowledge, we adopt entropy-based fusion scoring for zero-shot fusion. Q2E outperforms the previous SOTA on the MultiVENT dataset by 8 NDCG points, while improving on MSR-VTT and MSVD by 4 and 3 points, respectively, outperforming several existing retrieval methods, including many fine-tuned and SOTA zero-shot approaches. We have released both code and data.