Prior Knowledge Integration via LLM Encoding and Pseudo Event Regulation for Video Moment Retrieval

TL;DR

This paper explores using large language model encoders to incorporate prior knowledge and pseudo-events into video moment retrieval, improving inter-concept relation modeling and achieving state-of-the-art results.

Contribution

It introduces a novel framework utilizing LLM encoders for refining multimodal embeddings in VMR, overcoming limitations of decoders and transferring refinement capabilities to other embeddings.

Findings

LLM encoders effectively refine inter-concept relations in multimodal embeddings.

Refinement capabilities transfer to embeddings like BLIP and T5 with similar inter-concept patterns.

Achieved state-of-the-art performance in video moment retrieval.

Abstract

In this paper, we investigate the feasibility of leveraging large language models (LLMs) for integrating general knowledge and incorporating pseudo-events as priors for temporal content distribution in video moment retrieval (VMR) models. The motivation behind this study arises from the limitations of using LLMs as decoders for generating discrete textual descriptions, which hinders their direct application to continuous outputs like salience scores and inter-frame embeddings that capture inter-frame relations. To overcome these limitations, we propose utilizing LLM encoders instead of decoders. Through a feasibility study, we demonstrate that LLM encoders effectively refine inter-concept relations in multimodal embeddings, even without being trained on textual embeddings. We also show that the refinement capability of LLM encoders can be transferred to other embeddings, such as BLIP and T5, as long as these embeddings exhibit similar inter-concept similarity patterns to CLIP embeddings. We present a general framework for integrating LLM encoders into existing VMR architectures, specifically within the fusion module. Through experimental validation, we demonstrate the effectiveness of our proposed methods by achieving state-of-the-art performance in VMR. The source code can be accessed at https://github.com/fletcherjiang/LLMEPET.