Vision Transformer Token Gating for Real-Time Video Inference
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Solution Overview
Problem
Vision transformers face high computational costs, limiting their deployment on resource-constrained or latency-critical devices, especially in scenarios requiring real-time processing of large image/video files.
Innovation Solution
A method that leverages temporal redundancy in frame/video recognition by selectively updating tokens that have undergone significant changes, using token gating modules to reduce computational cost while preserving accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If transformers are applied to process video frames, then recognition accuracy is improved, but computational cost increases significantly
Solution Approach 1:
The video sequence is segmented into key frames and non-key frames. Only key frames are processed by the transformer model, while non-key frames are handled through temporal interpolation or simplified processing. This segmentation allows the system to maintain high recognition accuracy on important frames while significantly reducing computational cost by avoiding full transformer processing on every frame.
Solution Approach 2:
Instead of applying the full transformer model to every video frame, the system applies partial processing by selecting only certain frames (key frames) for complete transformer analysis. Non-key frames receive reduced processing, such as temporal interpolation from neighboring key frames or simplified feature extraction, thereby reducing overall computational burden while preserving necessary recognition accuracy.
2Measurement precision
If transformers process every frame in real-time, then recognition accuracy is maintained, but processing speed decreases
Solution Approach 1:
The video stream is segmented into key frames requiring full transformer processing and non-key frames requiring minimal processing. This segmentation enables the system to maintain high recognition accuracy on key frames while achieving real-time processing speeds by dramatically reducing the number of frames subjected to computationally intensive transformer operations.
Solution Approach 2:
The system employs periodic key frame detection and selection, where only periodically identified key frames undergo complete transformer processing. Between key frames, the system uses temporal interpolation or simplified processing, creating a periodic pattern of full and reduced processing that maintains accuracy while enabling real-time performance.
3Reliability
If transformers process large video files, then recognition completeness is improved, but memory usage increases
Solution Approach 1:
The system extracts and processes only key frames from large video files using the transformer model, rather than loading and processing every frame. This extraction approach maintains recognition completeness by ensuring all important moments are captured and analyzed, while significantly reducing memory usage by avoiding the need to hold entire video sequences in memory simultaneously.
Solution Approach 2:
The system performs preliminary key frame detection and selection before applying the transformer model. By pre-identifying which frames require full processing, the system can allocate memory efficiently, loading only necessary frames into memory for transformer analysis rather than attempting to load and process the entire video file, thus maintaining recognition completeness with reduced memory requirements.
Data Source
AI summary
Methods and systems for processing frame data are disclosed. The methods and systems include: obtaining a sequence of data, the sequence comprising at least a first frame; applying the sequence of data to a machine learning model; and providing an inference result based on the machine learning output score from the machine learning model. The machine learning model is configured to: determine at least one token-level error based on a difference between at least one token of a first representation corresponding to the first frame and a second representation; perform a first transformer operation based on the token subset to produce an output representation corresponding to the token subset; scatter the output representation to a buffer representation corresponding to the first representation; and produce a machine learning output score based on the buffer representation. Other aspects, embodiments, and features are also claimed and described.