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Multi-method Integration with Confidence-based Weighting for Zero-shot Image Classification

Siqi Yin, Lifan Jiang

TL;DR

The paper tackles zero-shot image classification by integrating multiple models and generation tools to overcome information bottlenecks for unseen categories. It generates boundary-focused reference images via ChatGPT and DALL-E, aligns images and text with CLIP, augments with DINO-based image-image alignment, and fuses predictions using confidence-based weights. The method achieves strong results on CIFAR-10/100 and TinyImageNet, with AUROC consistently above 96% and CIFAR-10 surpassing 99%, demonstrating improved generalization to open-set categories. This multi-model, boundary-aware fusion approach offers a practical path toward robust zero-shot and open-set recognition in diverse visual domains.

Abstract

This paper introduces a novel framework for zero-shot learning (ZSL), i.e., to recognize new categories that are unseen during training, by using a multi-model and multi-alignment integration method. Specifically, we propose three strategies to enhance the model's performance to handle ZSL: 1) Utilizing the extensive knowledge of ChatGPT and the powerful image generation capabilities of DALL-E to create reference images that can precisely describe unseen categories and classification boundaries, thereby alleviating the information bottleneck issue; 2) Integrating the results of text-image alignment and image-image alignment from CLIP, along with the image-image alignment results from DINO, to achieve more accurate predictions; 3) Introducing an adaptive weighting mechanism based on confidence levels to aggregate the outcomes from different prediction methods. Experimental results on multiple datasets, including CIFAR-10, CIFAR-100, and TinyImageNet, demonstrate that our model can significantly improve classification accuracy compared to single-model approaches, achieving AUROC scores above 96% across all test datasets, and notably surpassing 99% on the CIFAR-10 dataset.

Multi-method Integration with Confidence-based Weighting for Zero-shot Image Classification

TL;DR

The paper tackles zero-shot image classification by integrating multiple models and generation tools to overcome information bottlenecks for unseen categories. It generates boundary-focused reference images via ChatGPT and DALL-E, aligns images and text with CLIP, augments with DINO-based image-image alignment, and fuses predictions using confidence-based weights. The method achieves strong results on CIFAR-10/100 and TinyImageNet, with AUROC consistently above 96% and CIFAR-10 surpassing 99%, demonstrating improved generalization to open-set categories. This multi-model, boundary-aware fusion approach offers a practical path toward robust zero-shot and open-set recognition in diverse visual domains.

Abstract

This paper introduces a novel framework for zero-shot learning (ZSL), i.e., to recognize new categories that are unseen during training, by using a multi-model and multi-alignment integration method. Specifically, we propose three strategies to enhance the model's performance to handle ZSL: 1) Utilizing the extensive knowledge of ChatGPT and the powerful image generation capabilities of DALL-E to create reference images that can precisely describe unseen categories and classification boundaries, thereby alleviating the information bottleneck issue; 2) Integrating the results of text-image alignment and image-image alignment from CLIP, along with the image-image alignment results from DINO, to achieve more accurate predictions; 3) Introducing an adaptive weighting mechanism based on confidence levels to aggregate the outcomes from different prediction methods. Experimental results on multiple datasets, including CIFAR-10, CIFAR-100, and TinyImageNet, demonstrate that our model can significantly improve classification accuracy compared to single-model approaches, achieving AUROC scores above 96% across all test datasets, and notably surpassing 99% on the CIFAR-10 dataset.
Paper Structure (22 sections, 9 equations, 4 figures, 8 tables)

This paper contains 22 sections, 9 equations, 4 figures, 8 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Zero-Shot Classification
  4. Clip
  5. Dino
  6. GPT
  7. Data Augmentation
  8. Method
  9. CLIP Model
  10. Reference Images Generation
  11. Classification Prediction
  12. Prediction Fusion
  13. Experiments
  14. Datasets and Evaluation Metrics
  15. Main Results
  16. ...and 7 more sections

Figures (4)

  • Figure 1: Overview of our method.
  • Figure 2: Examples of utilizing ChatGPT to generate common features of similar categories.
  • Figure 3: Examples of synthesized reference images for similar categories with similar appearance.
  • Figure 4: Examples of t-SNE results for CLIP image-text alignment, CLIP image-image alignment and DINO image-image alignment.