VLM EOS Token Control for Concise Video Captioning
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Video analysis using vision language models (VLMs) is computationally intensive and generates redundant or irrelevant text captions, especially for long-form videos with multiple scenes, due to resource-intensive processing and variations in caption length, leading to operational inefficiencies.
Innovation Solution
Fine-tuning a VLM after distillation of a training set to generate concise text captions, dynamically adjusting the probability of an end-of-sentence token, and selectively applying captioning to representative units of video.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If a VLM is applied to every frame of video to generate captions, then comprehensive video content description is improved, but computational resource consumption and processing time increase significantly
Solution Approach 1:
The video is segmented into representative frames rather than processing every frame. The system identifies and selects key frames that capture essential video content, applying VLM captioning only to these segmented portions. This reduces computational load while preserving meaningful video information.
Solution Approach 2:
Instead of applying VLM to all frames (excessive action), the system applies captioning to a partial subset of representative frames. This partial action approach maintains adequate video description quality while significantly reducing processing requirements.
2Loss of information
If captions are generated for every frame of video, then detailed video coverage is improved, but redundant and irrelevant text generation increases
Solution Approach 1:
The video processing is segmented to identify only representative frames that contain meaningful content changes. By processing only these segmented portions rather than continuous frames, the system eliminates redundant caption generation while maintaining adequate video coverage.
Solution Approach 2:
The system extracts and identifies representative frames from the video sequence based on content significance. By taking out only these essential frames for captioning, the system removes redundant processing of non-representative frames that would generate unnecessary text.
3Loss of information
If the VLM generates text captions for all video frames, then comprehensive description is improved, but operational costs and processing time increase
Solution Approach 1:
The video is divided into segments represented by key frames. The system processes only these segmented representative frames through the VLM, significantly reducing the total number of captioning operations and associated processing time while maintaining description quality.
Solution Approach 2:
The system performs partial captioning action on a subset of representative frames rather than exhaustive captioning of all frames. This partial approach reduces processing time and operational costs while preserving essential video information.
Data Source
AI summary
A vision language model (“VLM”) generates text captions from video content. Innovations in controlling the complexity of captioning that uses a VLM are described. For example, a training tool updates a training set so that text captions are more concise, then fine-tunes a VLM using the updated training set. Or, as another example, a generative artificial intelligence model such as a VLM dynamically adjusts the probability of an end-of-sentence (“EOS”) token so that the probability of the EOS token increases in successive iterations of output token generation, which tends to make generated text captions more concise. Or, as another example, a captioning tool identifies and ranks representative units (such as keyframes) of video, then selectively applies captioning (using a VLM) to representative units of the video based on ranking information. Together or individually, the innovations can improve the computational efficiency and accuracy of captioning that uses a VLM.