VLM Video Captioning With Concise Output and Keyframe Selection
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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, leading to inefficiencies in resource usage and operational costs.
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 achieved, but computational resources and time are excessively consumed
Solution Approach 1:
The video is segmented into representative units (e.g., keyframes or short clips) rather than processing every frame. This segmentation allows the VLM to focus on pivotal moments that capture essential video content, reducing computational load while preserving descriptive completeness.
Solution Approach 2:
Instead of applying captioning to all frames (excessive action), the system selectively applies VLM captioning only to representative units that meet certain criteria (partial action). This selective approach maintains information quality while improving processing efficiency.
2Loss of information
If captions are generated for every frame of video, then detailed video coverage is achieved, but redundant or irrelevant text is generated
Solution Approach 1:
The video sequence is divided into representative units that capture essential content. By generating captions only for these segmented units rather than every frame, the system avoids redundant text while maintaining comprehensive coverage of meaningful video content.
Solution Approach 2:
Different quality standards are applied to different video segments. Representative units that capture pivotal moments receive detailed captioning, while less significant frames are either captioned more briefly or skipped entirely, optimizing text quality across the entire video.
3Loss of information
If VLM captioning is applied to all video frames, then complete video documentation is achieved, but operational costs increase
Solution Approach 1:
The video is segmented into representative units for captioning, reducing the total number of VLM inferences required. This segmentation maintains documentation completeness for essential video content while significantly reducing operational costs associated with VLM processing.
Solution Approach 2:
The system performs partial captioning only on representative video units rather than all frames. This selective approach achieves sufficient video documentation at a fraction of the operational cost of comprehensive frame-by-frame captioning.
4Loss of information
If captions are generated for non-representative frames, then frame-level coverage is achieved, but caption consistency and readability deteriorate
Solution Approach 1:
The video is segmented into representative units that ensure each captioned segment contains meaningful content. This segmentation approach maintains frame-level coverage for essential moments while ensuring caption consistency and readability by avoiding captions on non-representative frames.
Data Source
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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.