Cross-Modal Few-Shot Learning with Second-Order Neural Ordinary Differential Equations

• URL: http://arxiv.org/abs/2412.15813v1
• Date: Fri, 20 Dec 2024 11:42:41 GMT
• Title: Cross-Modal Few-Shot Learning with Second-Order Neural Ordinary Differential Equations
• Authors: Yi Zhang, Chun-Wun Cheng, Junyi He, Zhihai He, Carola-Bibiane Schönlieb, Yuyan Chen, Angelica I Aviles-Rivero,
• Abstract summary: We introduce SONO, a novel method leveraging Second-Order Neural Ordinary Differential Equations (Second-Order NODEs) to enhance cross-modal few-shot learning. Our second-order approach can approximate a broader class of functions, enhancing the model's expressive power and feature generalization capabilities. We utilize text-based image augmentation, exploiting CLIP's robust image-text correlation to enrich training data significantly.
• Score: 26.46540034821343
• License:
• Abstract: We introduce SONO, a novel method leveraging Second-Order Neural Ordinary Differential Equations (Second-Order NODEs) to enhance cross-modal few-shot learning. By employing a simple yet effective architecture consisting of a Second-Order NODEs model paired with a cross-modal classifier, SONO addresses the significant challenge of overfitting, which is common in few-shot scenarios due to limited training examples. Our second-order approach can approximate a broader class of functions, enhancing the model's expressive power and feature generalization capabilities. We initialize our cross-modal classifier with text embeddings derived from class-relevant prompts, streamlining training efficiency by avoiding the need for frequent text encoder processing. Additionally, we utilize text-based image augmentation, exploiting CLIP's robust image-text correlation to enrich training data significantly. Extensive experiments across multiple datasets demonstrate that SONO outperforms existing state-of-the-art methods in few-shot learning performance.

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