Video Bookmarking Engine Using Visual-Language Model Embeddings
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Solution Overview
Problem
The manual process of creating video bookmarks is tedious and time-consuming, leading to increased latency and resource consumption, which negatively impacts the efficiency and adoption of video sharing platforms.
Innovation Solution
A video-editing tool with a bookmarking engine that uses a visual-language model to embed user prompts and video frames into a shared vector space, allowing for intelligent bookmark generation by identifying frames semantically related to the prompt, thereby reducing the need for manual intervention and optimizing resource usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual bookmark creation is used, then accuracy and relevance of bookmarks can be controlled, but time consumption and labor requirements increase significantly
Solution Approach 1:
The system enables self-service bookmark creation by automatically analyzing video content and generating bookmarks without requiring manual user intervention. The automated system extracts key moments, generates descriptions, and creates bookmarks independently, eliminating the time-consuming manual process while maintaining acceptable accuracy through intelligent algorithms.
Solution Approach 2:
The patent replaces the mechanical manual process of watching and selecting bookmarks with an automated system using image encoders, text encoders, and similarity computation. This substitution of mechanical human labor with automated computational systems dramatically reduces time consumption while maintaining bookmark quality through sophisticated algorithms.
2Loss of time
If automated bookmark generation is implemented, then time consumption is reduced, but computational resource requirements increase
Solution Approach 1:
The system segments the video into smaller frames and processes them individually through the image encoder. By dividing the large video data into manageable units (frames) and processing them in batches, the system reduces peak computational resource requirements while maintaining comprehensive analysis capability. This segmentation allows parallel processing and reduces overall computational burden.
Solution Approach 2:
The system uses subsampling to process only a subset of video frames rather than all frames, which reduces computational resource consumption. By selectively processing key frames or a representative sample, the system achieves sufficient bookmark generation accuracy with significantly lower computational costs than would require full-frame processing.
3Measurement precision
If more video frames are processed for bookmark generation, then bookmark accuracy improves, but computational complexity and processing time increase
Solution Approach 1:
The system employs subsampling techniques to process only a representative subset of video frames rather than all frames. This partial processing approach maintains adequate bookmark accuracy by focusing computational resources on the most informative frames while avoiding the excessive complexity and time consumption of processing every frame in detail.
Solution Approach 2:
The system dynamically adjusts processing parameters such as the number of frames to process, the resolution at which frames are analyzed, and the similarity threshold for bookmark selection. By optimizing these parameters based on video length, content type, and computational resources available, the system achieves high accuracy without excessive processing complexity.
Data Source
AI summary
A method for generating a video bookmark is provided. The method can include receiving a prompt comprising a description associated with a target scene within a video, generating a prompt embedding from the received prompt, identifying, based on a set of image embeddings each corresponding to a frame of the video, an image embedding corresponding to the prompt embedding, and generating a video bookmark at a frame associated with the identified image embedding.