VLM Video Captioning with EOS Control 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 variations in caption length 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 (scenes or shots) rather than processing every frame. The system identifies keyframes or representative frames within each unit and applies VLM captioning only to these selected frames, significantly reducing computational load while preserving essential video content information.
Solution Approach 2:
Instead of applying VLM to all frames (excessive action), the system applies captioning selectively to representative frames only (partial action). This partial application suffices to capture the essential video content without the unnecessary computational overhead of processing every single frame.
2Loss of information
If captions are generated for every frame of video, then complete video coverage is achieved, but redundant or irrelevant text is generated
Solution Approach 1:
The video is divided into meaningful units (scenes or shots) with distinct semantic content. By generating captions at the unit level rather than frame level, the system avoids redundant text generation while ensuring each unique video segment is represented by at least one caption.
Solution Approach 2:
The system applies captioning only where necessary - to representative frames that capture distinct video units - rather than applying it universally to all frames. This selective approach eliminates redundant text generation while maintaining complete video coverage.
3Loss of information
If VLM generates detailed captions for all video content, then comprehensive description is achieved, but caption length variations create consistency problems
Solution Approach 1:
By segmenting video into units and selecting representative frames, the system naturally controls caption length variability. Each unit's representative frame generates a caption of comparable length, as they all represent similar levels of semantic importance and content density.
Solution Approach 2:
The selective application of VLM to representative frames only ensures that caption length remains consistent across different video segments. This partial action approach avoids the extreme length variations that would occur if every frame were captioned, as only frames of comparable representativeness are processed.
4Loss of information
If VLM is applied to long-form video with multiple scenes, then complete video analysis is achieved, but operational costs increase unexpectedly
Solution Approach 1:
Long-form video is segmented into multiple scenes or shots, and the system processes only representative frames from each segment. This segmentation strategy maintains complete video analysis coverage while dramatically reducing the total number of VLM inferences required, thus lowering operational costs.
Solution Approach 2:
The system applies VLM captioning partially - only to representative frames that capture essential video content - rather than applying it excessively to every frame throughout the entire long-form video. This partial application achieves complete analysis coverage at a fraction of the operational cost.
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.