Image-to-Video Bootstrapping for Computer Vision Training
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Solution Overview
Problem
Modern computer vision systems require large-scale annotated video datasets, which are time-consuming and resource-intensive to create, limiting their speed and accuracy in video understanding applications.
Innovation Solution
A novel image-to-video bootstrapping technique that reduces annotation time and computational resources by iteratively training a visual recognition model using image datasets and unlabeled video data, with machine-in-the-loop feedback for improving label accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If visual recognizers are trained on image datasets and applied to video domain, then training speed is improved, but accuracy deteriorates due to different visual characteristics between images and videos
Solution Approach 1:
The patent introduces an intermediary representation called 'video-like images' that are generated from actual video frames. These intermediary images retain the compression artifacts and visual characteristics of video while being processable as images. The visual recognizer trained on these intermediary images acts as a bridge, allowing image-based training methods to work effectively on video data without sacrificing accuracy.
Solution Approach 2:
The patent transforms the training data parameters by converting actual video frames into 'video-like images' through controlled compression and processing. This parameter transformation allows the training data to maintain the essential visual characteristics of video (including compression artifacts) while being compatible with image-based training pipelines, thus resolving the mismatch between training data and target application domain.
2Measurement precision
If video datasets are annotated by human labelers inspecting every frame, then label accuracy is improved, but time consumption and computational resources increase significantly
Solution Approach 1:
The patent performs preliminary action by automatically generating 'video-like images' from video frames before the annotation process. These pre-processed images serve as ready-to-use training data that captures the essential visual characteristics of video. This preliminary processing eliminates the need for manual frame-by-frame inspection while maintaining annotation quality, as the generated images can be directly used for training visual recognizers.
Solution Approach 2:
The system performs self-service by automatically transforming video frames into annotated training data through the video-like image generation process. The computational pipeline autonomously handles the conversion and preparation of training data without requiring human intervention for each frame, thereby reducing annotation time and computational resource requirements while maintaining accuracy.
3Quantity of substance
If video frames are compressed using codecs to reduce file size, then storage efficiency is improved, but visual quality deteriorates with blurry frames
Solution Approach 1:
The patent converts the harmful effect of compression artifacts into a benefit by deliberately generating 'video-like images' that incorporate controlled compression. Instead of treating compression artifacts as noise to be eliminated, the methodology embraces them as essential characteristics that make the training data more representative of actual video conditions. This allows the visual recognizer to learn from data that closely mimics real-world video quality, improving robustness while working with compressed video files.
Data Source
AI summary
Disclosed are systems and methods for improving interactions with and between computers in content searching, hosting and/or providing systems supported by or configured with devices, servers and/or platforms. The disclosed systems and methods provide a novel machine-in-the-loop, image-to-video bootstrapping framework that harnesses a training set built upon an image dataset and a video dataset in order to efficiently produce an accurate training set to be applied to frames of videos. The disclosed systems and methods reduce the amount of time required to build the training dataset, and also provide mechanisms to apply the training dataset to any type of content and for any type of recognition task.