Hierarchically Robust Zero-shot Vision-Language Models
Summary: arXiv:2604.18867v1 Announce Type: cross
Abstract
Vision-Language Models (VLMs) have demonstrated impressive capabilities in performing zero-shot classification tasks. However, they are notably vulnerable to adversarial attacks, which can significantly undermine their performance. While robust fine-tuning methods can enhance their resilience, many existing strategies involve aligning fixed text embeddings with image embeddings, which often leads to a compromise in both natural performance and robustness. Additionally, a degradation in robustness is observed when models encounter adversarial attacks that target superclasses (such as mammals) alongside their specific base (leaf) classes (such as cats). This paper introduces a novel adversarial fine-tuning framework that leverages hierarchical embeddings to improve adversarial robustness while utilizing the inherent hierarchical properties of class space.
Key Innovations
- Hierarchical Embeddings: The proposed model employs a structure that reflects the hierarchical organization of classes, allowing for more nuanced alignment between image and text modalities.
- Adversarially Robust Alignment: Multiple levels of alignment are introduced to create a more robust framework that can withstand adversarial attacks targeting various levels of the hierarchy.
- Visual Embedding Depth: The model implements mechanisms that position visual embeddings at the appropriate depth within the hierarchy, enhancing the model’s overall robustness.
- Theoretical Connections: A theoretical relationship is established between the depth of embeddings in the hierarchy and the maximum viable margin size, leading to improved generalization capabilities.
- Semantic Variety: By aligning across multiple trees that share leaf labels, the model increases semantic diversity, further enhancing its robustness against adversarial attacks.
Methodology
The proposed framework involves a multi-tiered approach to adversarial fine-tuning. It begins by establishing a hierarchy of class embeddings, which is essential for capturing the relationships between superclasses and their respective subclasses. The model then applies robust alignment strategies at multiple levels, ensuring that both image and text modalities are aligned in a manner that reflects their hierarchical relationships.
One of the critical innovations of this framework is the incorporation of visual embeddings at various depths within the hierarchy. This depth positioning is crucial, as it allows the model to adaptively respond to different adversarial threats based on their proximity within the hierarchy. The theoretical framework supporting these innovations provides insights into how depth influences margin sizes, ultimately contributing to the model’s robustness.
Experimental Results
Extensive experiments were conducted across several datasets to evaluate the efficacy of the proposed model. Results indicate significant improvements in adversarial robustness compared to traditional VLMs, particularly in scenarios where adversarial attacks targeted superclass levels. The model’s ability to leverage hierarchical relationships not only enhanced its performance but also demonstrated a marked increase in generalization capabilities, making it a promising direction for future research in vision-language models.
Conclusion
The introduction of a hierarchically robust zero-shot vision-language model presents a significant advancement in the field of adversarial robustness. By harnessing the power of hierarchical embeddings and multiple levels of alignment, this approach paves the way for more resilient models capable of withstanding sophisticated adversarial attacks, thus enhancing the reliability of vision-language applications.