Visual Language Model Context Training for Few-Shot Image Categorization
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Solution Overview
Problem
Small visual language models lack the ability to effectively perform in-context learning due to reliance on semantic priors, making them unable to adapt to new prompts without gradient-based updates.
Innovation Solution
A method involving deep image clustering, assigning semantically unrelated names to clusters, generating image captions, and constructing a self-supervised context for training, which mimics in-context learning without labeled data, allowing small VLMs to learn patterns from context.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If small visual language models are used, then model size and computational resources are reduced, but the ability to perform in-context learning deteriorates
Solution Approach 1:
The patent applies preliminary action by pre-training small VLMs on clustered image data with generated captions before deployment. The images are clustered using k-means clustering, and captions are generated in advance using the visual language model. This pre-processing creates a structured training corpus that enables small models to develop in-context learning capabilities without requiring large parameter counts during actual inference.
Solution Approach 2:
The patent implements self-service through self-supervised learning where the visual language model generates its own training data. The model clusters images, generates captions for clustered images, and uses these self-generated captions to fine-tune itself. This self-service mechanism allows small VLMs to adapt to specific domains and develop in-context learning abilities without requiring extensive external labeled datasets or large-scale pre-training resources.
2Adaptability or versatility
If large language models are used for in-context learning, then learning capability is improved, but parameter scaling and pre-training data requirements increase
Solution Approach 1:
The patent applies segmentation by dividing the training process into distinct stages: (1) clustering images using k-means algorithm, (2) generating captions for clustered images, (3) fine-tuning the VLM on this structured data. This segmentation allows small models to learn in-context capabilities through targeted, structured training rather than requiring massive general-purpose pre-training data, thus reducing both parameter count and data volume requirements.
Solution Approach 2:
The patent utilizes parameter changes by transforming the training approach from standard supervised learning to self-supervised learning on clustered data. The visual language model's parameters are fine-tuned on self-generated captions from clustered images, which changes the optimization landscape and enables small models to achieve in-context learning capabilities with fewer parameters by learning from structurally organized data rather than raw text-corpus-scale data.
Data Source
AI summary
Methods, systems, and techniques for image categorization using a visual language model. A set of images is clustered into clusters respectively corresponding to differently categorized objects. Names are respectively assigned to the clusters, and image captions are respectively generated for the clusters using the names. The image captions and respective images represent image-text pairs. Those image-text pairs are input to the visual language model as context for a query. The query is then input to the visual language model. The query includes a request to categorize a query image of a class represented in the context. In response to the query, the visual language model performs an open-ended generative categorization of the query image.