Long Video Retrieval With Diverse Caption Training
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Solution Overview
Problem
Existing long video retrieval systems using Large Language Models (LLMs) fail to effectively model the variety of captions and capture the richness and variety of valid descriptions for long videos, leading to ambiguous and vague queries, and neglecting the unique characteristics of videos in datasets.
Innovation Solution
Generate a plurality of captions of varying dimensions using LLMs, associate these captions with video datasets to create enhanced video datasets, and finetune a pretrained video language model using contrastive loss functions to improve retrieval performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a single long paragraph is used to describe every long video, then the video retrieval system can be trained and tested in the paragraph-to-video regime, but the richness and variety of possible valid descriptions of the video are neglected
Solution Approach 1:
The patent segments a single long paragraph description into multiple diverse captions of varying lengths and styles. Instead of treating every video with one uniform paragraph description, the system generates multiple caption variations that capture different aspects and levels of detail, thereby preserving the richness of video descriptions while improving adaptability to various query types.
Solution Approach 2:
The patent adds a new dimension to video descriptions by generating captions that vary in length, style, and level of detail. This transforms the single-dimension paragraph description into a multi-dimensional caption set, allowing the retrieval system to handle diverse queries more effectively while maintaining comprehensive video information.
2Adaptability or versatility
If existing video retrieval datasets use only full paragraphs for retrieval, then the system can maintain simplicity in data structure, but the rich space of valid captions including vague, abstract, or partial descriptions is neglected
Solution Approach 1:
The patent introduces dynamic caption generation that adapts to different query requirements. Instead of a static single-paragraph structure, the system dynamically generates multiple caption variations with different properties (length, style, detail level), enabling the retrieval system to flexibly match diverse user queries while managing data complexity through systematic generation processes.
Solution Approach 2:
The patent changes the parameters of video descriptions by generating captions with varying lengths, styles, and levels of abstraction. This allows the system to represent the same video content in multiple parameter configurations, improving query handling capability while maintaining structured data organization through controlled parameter variations.
3Ease of manufacture
If current video retrieval systems use LLMs trained on paragraph-to-video regime, then the training process can be straightforward, but the systems fail to model the variety of captions and show how they can be improved
Solution Approach 1:
The patent applies preliminary action by pre-generating diverse caption variations for training data before the actual retrieval task. This preparation step creates a comprehensive training dataset with multiple caption styles and lengths, enabling the LLM to learn robust video-description mappings that improve retrieval reliability while keeping the training process manageable through structured data preparation.
Solution Approach 2:
The patent uses copying by generating multiple caption copies of the same video content with different styles and levels of detail. These copied captions serve as additional training examples that help the LLM learn to handle various query types, improving retrieval performance without requiring entirely new training processes.
Data Source
AI summary
Embodiments of the present principles generally relate to methods, apparatuses, and systems for improved long video retrieval by training video language models (VLM) using diverse captions. In some embodiments, a method for improved long video retrieval may include generating a plurality of captions of varying dimensions using one or more Large Language Models (LLM); associating the plurality of captions of varying dimensions to one or more videos in one or more video data sets to generate one or more enhanced video data sets; generating an enhanced VLM by finetuning a pretrained video language model using the generated one or more enhanced video data sets; and retrieving one or more videos with a query using the enhanced VLM having a R@K rank.