Computer Vision Model Training Across Multiple Teacher Resolutions
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Solution Overview
Problem
Existing computer vision models face challenges due to varying resolutions and biased learning from teacher models with different architectures and training goals, leading to inconsistent feature granularity and poor generalization across tasks.
Innovation Solution
A multi-resolution and multi-teacher training method is employed, where a student model is trained across multiple resolutions from a plurality of teacher models, with normalization techniques to align distributions and reverse normalization at inference time, and token compression to enhance performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If teacher models operate at varying resolutions due to different architectures and training goals, then each teacher model can be optimized for its specific task, but feature granularity inconsistencies arise
Solution Approach 1:
The patent segments the feature extraction process by using multiple teacher models at different resolutions (e.g., high-resolution for fine details, low-resolution for coarse structures) and then combines their outputs through a fusion mechanism. This allows each teacher to specialize in specific granularity levels while the student model learns to integrate them, resolving the contradiction between task optimization and feature consistency.
Solution Approach 2:
The patent introduces a resolution dimension by training the student model to process and fuse features from multiple resolution levels simultaneously. Instead of forcing all teachers to operate at the same resolution, the system adds resolution as an additional dimension in the feature space, allowing the student to learn resolution-invariant representations that maintain consistency across different granularities.
2Adaptability or versatility
If existing models have different distribution moments, then each model can capture its specific data characteristics, but biased learning results
Solution Approach 1:
The patent applies normalization techniques to transform the output distributions of different teacher models into a common reference frame with matched moments (mean, variance). This creates equipotential conditions where all teachers contribute equally to the student's learning process, preventing any single teacher's distribution characteristics from dominating and causing bias, while still preserving the essential data characteristics each teacher captures.
3Measurement precision
If computer vision models are trained to produce features at a particular resolution, then the model achieves high accuracy at that resolution, but generalization to different resolutions is poor
Solution Approach 1:
The patent trains a single student model to perform multiple functions: it learns to process and fuse features from teacher models at various resolutions and can output features at multiple resolution levels. This multi-functional training approach enables the student to generalize across resolutions by learning resolution-invariant feature representations that can be adapted to different output requirements, maintaining accuracy while gaining versatility.
Data Source
AI summary
The rise of specialized vision foundation models has created a need for methods to consolidate knowledge from multiple models (i.e. the teachers) into a single model (i.e. the student). However, this type of knowledge agglomeration leaves open several critical challenges, including that teacher models typically operate at varying resolutions due to different architectures and training goals, creating feature granularity inconsistencies, that existing models have different distribution moments which can result in biased learning, and that computer vision models are oftentimes trained to produce features at a particular resolution, and therefore do not generalize well to different tasks requiring different resolutions. The present disclosure provides multi-resolution and multi-teacher based training of a computer vision model, which can capture both fine details and broader abstractions from the teacher models, which can prevent biased learning among the teacher models, and which can produce a flexible computer vision model for different feature resolutions.