Video Title Generation Using Language and Scene Feature Matching
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
General large language models fail to generate appropriate video titles that accurately match the content due to lack of training on specific video fields, resulting in semantically reasonable but mismatched titles.
Innovation Solution
A video title generation method using a combination of a trained language model and a neural network model tailored to the video type, identifying language and scene features to generate titles that better align with the video content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a general large language model is used to generate video titles, then the model has powerful text generation capabilities and can generate relevant title sentences, but the generated titles do not accurately match the video content due to lack of training on specific video fields
Solution Approach 1:
The patent segments the title generation task into two distinct sub-models: a language model for generating candidate titles and a video understanding model for evaluating content matching. This segmentation allows each model to specialize in its strength while resolving the contradiction between generation capability and matching accuracy.
Solution Approach 2:
The patent introduces an intermediary evaluation mechanism where the video understanding model acts as a mediator to assess the relevance between generated titles and video content. This intermediary layer ensures that titles not only are linguistically sound but also accurately reflect the video content, resolving the matching accuracy issue.
2Device complexity
If a single language model is used for title generation, then the model structure is simple, but the model cannot capture both language features and scene features of video content
Solution Approach 1:
The patent divides the feature extraction capability into two specialized sub-models: one for language features and another for scene features. This segmentation allows each model to be optimized for its specific function while maintaining overall system manageability.
Solution Approach 2:
The patent creates a multi-functional system where the two sub-models work together to provide both language understanding and visual scene understanding capabilities. This universal approach allows the system to handle diverse video content types while keeping individual model components relatively simple.
3Loss of information
If a title generation system is trained on large-scale general corpus, then the model learns rich language knowledge, but the model lacks understanding of specific video field characteristics
Solution Approach 1:
The patent segments the training data requirements into two parts: general language corpus for the language model and video-specific data for the video understanding model. This segmentation allows the system to retain rich language knowledge while also acquiring video field understanding through specialized training.
Solution Approach 2:
The patent applies local quality by training different models with different data qualities: the language model receives general corpus training for language proficiency, while the video understanding model receives video-specific training for field understanding. Each model has optimized training data quality for its specific function.
Data Source
AI summary
The present disclosure relates to the field of multi-modality content processing, and a video title generation method and apparatus, an electronic device, and a storage medium are disclosed. The method includes: obtaining a target video to be processed; extracting a target description content corresponding to the target video; and identifying, using a title generation model, a language feature and a scene feature of the target description content, and generating a target video title of the target video based on the language feature and the scene feature.