Multimodal Contextualized Support for Enhancing Video Retrieval System

TL;DR

This paper introduces a multimodal, context-aware video retrieval system that leverages multiple frames to better understand and retrieve videos based on higher-level, abstract information rather than just individual keyframes.

Contribution

It proposes a novel pipeline that integrates multimodal data and multiple frames to enhance video understanding and retrieval accuracy.

Findings

Improved retrieval accuracy by capturing higher-level video semantics.

Effective integration of multimodal data from multiple frames.

Enhanced understanding of actions and events over single-frame analysis.

Abstract

Current video retrieval systems, especially those used in competitions, primarily focus on querying individual keyframes or images rather than encoding an entire clip or video segment. However, queries often describe an action or event over a series of frames, not a specific image. This results in insufficient information when analyzing a single frame, leading to less accurate query results. Moreover, extracting embeddings solely from images (keyframes) does not provide enough information for models to encode higher-level, more abstract insights inferred from the video. These models tend to only describe the objects present in the frame, lacking a deeper understanding. In this work, we propose a system that integrates the latest methodologies, introducing a novel pipeline that extracts multimodal data, and incorporate information from multiple frames within a video, enabling the model to abstract higher-level information that captures latent meanings, focusing on what can be inferred from the video clip, rather than just focusing on object detection in one single image.