Adaptive Mixed-Resolution Tokenization for Transformer Efficiency
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Solution Overview
Problem
Deep learning machine learning models, particularly transformers, face inefficiencies due to the suboptimal tokenization process, where tokens carry no semantic meaning and the number of tokens increases quadratically with image size, leading to high computational costs and memory demands.
Innovation Solution
The implementation of a method that generates input tokens at different resolutions by dividing images into mixed-scale tokens, using a pre-processing engine to predict optimal tokenization scales for each region, reducing the number of tokens processed by the transformer model, thereby enhancing computational efficiency without compromising performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If uniform high-resolution tokens are used for the entire image, then detailed feature extraction is improved, but computational complexity and memory demands increase quadratically
Solution Approach 1:
The patent applies local quality by using mixed-resolution tokenization where different regions of the image are represented by tokens at different resolutions. Important regions (e.g., containing objects of interest) use higher resolution tokens for detailed feature extraction, while less important regions use lower resolution tokens to reduce computational load. This resolves the contradiction by making feature extraction accuracy local to where it is most needed rather than uniformly applying high resolution everywhere.
Solution Approach 2:
The patent segments the image into multiple regions with different importance levels and assigns different token resolutions to each segment. This segmentation approach allows the system to process only critical regions at high resolution, thereby reducing overall computational complexity while maintaining necessary feature extraction accuracy in important areas.
2Area of stationary object
If the number of tokens is increased to cover the entire image, then image coverage is improved, but processing time and memory demands increase
Solution Approach 1:
The patent changes the parameter of token resolution dynamically across different image regions. Instead of using a fixed high resolution for all tokens, the system varies the resolution parameter based on regional importance, thereby maintaining comprehensive image coverage while significantly reducing the total number of tokens that need to be processed, which directly reduces processing time.
3Productivity
If low-resolution tokens are used throughout, then computational efficiency is improved, but feature extraction accuracy deteriorates
Solution Approach 1:
The patent ensures that feature extraction accuracy is maintained locally in important regions by assigning high-resolution tokens specifically to those areas. This local quality approach allows the system to use lower resolution tokens in less critical regions for improved computational efficiency, while preserving necessary accuracy in regions where detailed features are essential for the task.
4Ease of manufacture
If uniform tokenization is applied, then implementation simplicity is maintained, but adaptability to different image regions deteriorates
Solution Approach 1:
The patent introduces dynamics into the tokenization process by making token resolution adaptive to different image regions based on their importance or semantic content. This dynamic approach allows the system to automatically adjust the level of detail in different regions, improving adaptability while maintaining relatively simple implementation through automated region classification and resolution assignment.
Data Source
AI summary
Systems and techniques are described for adaptive mixed-resolution processing. According to some aspects, a device can divide an input image into first tokens having a first resolution and second tokens having a second resolution. The device can generate first token representations for token(s) from the first tokens corresponding to a first region of the input image and generate second token representations for token(s) from the second tokens corresponding to the first region of the input image. The device can process, using a neural network model, the first token representations and the second token representations to determine the first resolution or the second resolution as a scale for the first region of the input image. The device can process, using a transformer neural network model, the first region of the input image according to the scale for the first region.