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Related Experiment Video

Updated: May 31, 2025

Using the Visual World Paradigm to Study Sentence Comprehension in Mandarin-Speaking Children with Autism

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Published on: October 3, 2018

Supporting vision-language model few-shot inference with confounder-pruned knowledge prompt.

Jiangmeng Li¹, Wenyi Mo², Fei Song³

¹National Key Laboratory of Space Integrated Information System, Institute of Software Chinese Academy of Sciences, Beijing, China.

Neural Networks : the Official Journal of the International Neural Network Society

|January 24, 2025

Summary

This study introduces Confounder-pruned Knowledge Prompt (CPKP), a novel method for vision-language models. CPKP enhances few-shot inference by incorporating semantic information into prompts, outperforming existing methods.

Keywords:

Knowledge graph Large-scale pre-training Maximum entropy Multi-modal model Prompt learning

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Area of Science:

Computer Vision
Natural Language Processing
Artificial Intelligence

Background:

Vision-language models align image-text data for open-set visual concepts.
Current prompting methods use fixed or learnable prompts to bridge pre-training and inference gaps.
The optimal prompt design for improving inference performance remains unclear.

Purpose of the Study:

To clarify the importance of semantic information in prompts for vision-language models.
To address the limitations of existing prompting methods that underutilize textual label semantics.
To propose an automated method for generating semantically rich prompts.

Main Methods:

Proposed Confounder-pruned Knowledge Prompt (CPKP), a knowledge-aware prompt learning method.
Utilized ontology knowledge graphs to extract semantic information from textual labels.
Implemented a double-tier confounder-pruning procedure (graph-tier and feature-tier) to refine semantic information.

Main Results:

CPKP demonstrated effectiveness in few-shot inference scenarios.
With only two shots, CPKP achieved superior performance compared to manual and learnable prompt methods.
CPKP outperformed the manual-prompt method by 4.64% and the learnable-prompt method by 1.09% on average.

Conclusions:

Incorporating rich semantic information via knowledge graphs significantly improves prompt performance.
CPKP offers an effective and automated approach to prompt engineering for vision-language tasks.
The proposed method advances few-shot learning capabilities in vision-language models.