Large-scale pre-trained vision-language models like CLIP exhibit strong generalizability but struggle with out-of-distribution (OOD) samples. A novel approach, OGEN, combines feature synthesis for unknown classes and adaptive regularization to address this, yielding improved performance across datasets and settings. OGEN showcases potential for addressing overfitting and enhancing both in-distribution (ID) and OOD performance.
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Introducing OGEN: A Novel AI Approach for Boosting Out-of-Domain Generalization in Vision-Language Models
Overview
Large-scale pre-trained vision-language models, like CLIP, show remarkable generalizability across diverse visual domains and real-world tasks. However, they face limitations in performance on certain downstream datasets. Recent efforts aim to enhance zero-shot out-of-distribution (OOD) detection and improve model regularization.
Key Features
- Combines image feature synthesis for unknown classes and an unknown-aware finetuning algorithm with effective model regularization
- Introduces class-conditional feature generator to synthesize image features for unknown classes based on CLIP’s image-text feature spaces
- Utilizes Multi-Head Cross-Attention (MHCA) to effectively capture similarities between unknown and known classes for feature synthesis
- Offers two feature synthesis methods: “extrapolating per class” and “extrapolating jointly,” with the latter consistently outperforming the former
- Includes adaptive self-distillation mechanism to reduce overfitting during joint optimization
Performance and Applications
OGEN consistently improves out-of-domain generalization performance across different datasets and settings, showcasing its potential to address overfitting and enhance both in-distribution and out-of-distribution performance. It enhances new class accuracy without compromising base class accuracy and demonstrates universality in improving generalization performance across different target datasets.
Practical Applications
For companies looking to evolve with AI, OGEN offers a practical solution to boost out-of-domain generalization in vision-language models. It provides a favorable trade-off between in-distribution and out-of-distribution performance, making it a valuable tool for enhancing model robustness and performance across diverse datasets and settings.
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