Vision-Language Prompt Learning With Reusable Context Vectors
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Solution Overview
Problem
Existing vision-language models, such as CLIP, face inefficiencies in zero-shot prediction tasks due to hand-crafted prompts that are not optimal for various classification problems, leading to high computation costs when learning one set of prompts for multiple tasks.
Innovation Solution
A Text-to-Classifier (TTC) system using a large language model and a prompt generator to convert text descriptions into context prompt vectors, aligning them with a vision-language model like CLIP, allowing customization for specific image classification tasks and generalizing across different classification problems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If one set of prompts is learned for multiple classification problems, then the model can handle various classification tasks, but the computation cost increases significantly
Solution Approach 1:
The prompt is segmented into two distinct components: task-agnostic context prompt vectors (capturing general image classification knowledge) and task-specific class text tokens (capturing problem-specific information). This segmentation allows the model to reuse the context prompt vectors across multiple classification tasks while only adapting the class text tokens for each specific task, thereby reducing computation cost while maintaining versatility.
Solution Approach 2:
The context prompt vectors serve as a universal component that can be applied across multiple classification problems. By learning this set of prompt vectors once and reusing them for different classification tasks, the model achieves multi-functionality without incurring significant additional computation costs for each new task.
2Adaptability or versatility
If traditional prompt learning is used for zero-shot prediction, then the model can predict unseen classes, but the computation time is excessive
Solution Approach 1:
The context prompt vectors are learned in advance as a preliminary step, capturing general knowledge about image classification. When performing zero-shot prediction on unseen classes, this pre-learned context is reused, eliminating the need to relearn prompts from scratch and significantly reducing computation time while maintaining the ability to predict unseen classes.
3Measurement precision
If the model learns prompts for each specific classification problem, then prediction accuracy improves, but the complexity of the system increases
Solution Approach 1:
The prompt structure is segmented into reusable context prompt vectors and task-specific class text tokens. This segmentation allows the system to maintain simplicity by reusing the context component across tasks while only adapting the class tokens for each specific classification problem, thereby improving prediction accuracy without proportionally increasing system complexity.
Data Source
AI summary
A method of generating text-driven prompts and class prediction probabilities using a vision-language model (VLM) includes receiving candidate class names associated with a plurality of candidate classes for images, generating class text tokens based on a text description of the candidate class names, and generating a plurality of context prompt vectors using a prompt generator. The context prompt vectors define context information associated with an image classification task to be performed by the VLM. The method further includes generating prompts for each of the plurality of candidate classes by appending respective class text tokens to the context prompt vectors for each of the plurality of candidate classes, and, using the VLM, generating and outputting a class prediction probability for a sample image based on the plurality of context prompt vectors.