Adaptive Tokenization Layer for Multi-Modal Model Efficiency
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Solution Overview
Problem
Current multi-modal learning models, particularly for tasks like Visual Question Answering, face inefficiencies and scalability issues due to the need to process large image sizes and complex interactions between image and text modalities, leading to computationally expensive and large models that are not scalable.
Innovation Solution
A novel multi-modal processing model with an adaptive tokenization layer that reduces feature data size by generating tokens from input features using convolutional layers and softmax operations, allowing for efficient fusion and processing of spatial and linear domain inputs, thereby conserving computational resources and improving model efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If Transformer architecture is used to process multi-modal data, then the model can effectively capture interactions between image and text modalities, but the computational cost and model size become prohibitively large
Solution Approach 1:
The patent extracts and processes only the most relevant features from each modality separately before fusion, rather than processing all features through the full Transformer architecture. This selective extraction reduces computational cost while maintaining the ability to capture important interactions.
Solution Approach 2:
The model segments the processing into distinct stages: separate feature extraction for image and text modalities, followed by selective fusion. This segmentation allows each modality to be processed independently with optimized computations, reducing overall computational cost while preserving interaction capture capabilities.
2Measurement precision
If image size is increased to improve processing quality, then better visual understanding is achieved, but the model becomes prohibitively large and computationally expensive
Solution Approach 1:
The patent extracts only the most salient and informative regions/features from images at appropriate sizes, rather than processing entire large images. This selective extraction maintains visual understanding quality while reducing model size and computational requirements.
Solution Approach 2:
The model applies partial processing to image data, focusing computational resources on the most relevant portions rather than uniformly processing the entire image. This allows good visual understanding with reduced computational overhead.
3Loss of information
If full feature sets are processed to maintain data completeness, then all information is preserved, but computational resources are not conserved and efficiency decreases
Solution Approach 1:
The patent extracts and retains only the most important and informative features from each modality, discarding redundant information. This selective feature extraction maintains sufficient information completeness for accurate multi-modal processing while dramatically improving computational efficiency.
Solution Approach 2:
The model applies different processing quality levels to different features based on their importance. Critical features are processed with high fidelity while less important features receive simplified processing, optimizing the balance between information retention and computational efficiency.
Data Source
AI summary
Provided is an efficient multi-modal processing model. The multi-modal processing model can process input data from multiple different domains to generate a prediction for a multi-modal processing task. A machine-learned multi-modal processing model can include an adaptive tokenization layer that is configured to adaptively tokenize features generated from the multi-modal inputs into sets of tokens. Specifically, the tokens may have a smaller data size relative to the features from the inputs, thereby enabling a reduced number of processing operations to be performed overall, thereby improving the efficiency of model.