Multi-resolution multi-teacher based training of a computer vision model

Abstract

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.

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