Hierarchically Robust Zero-shot Vision-language Models
📰 ArXiv cs.AI
Learn to build hierarchically robust zero-shot vision-language models that withstand adversarial attacks, improving both natural performance and robustness
Action Steps
- Build a vision-language model with hierarchical robustness using robust fine-tuning techniques
- Configure the model to align text embeddings with image embeddings dynamically
- Test the model's robustness against adversarial attacks targeting superclasses and base classes
- Apply hierarchical robustness to improve the model's performance on zero-shot classification tasks
- Compare the performance of the hierarchically robust model with existing robust fine-tuning approaches
Who Needs to Know This
Computer vision engineers and researchers can benefit from this knowledge to develop more robust vision-language models, while machine learning engineers can apply these concepts to improve model reliability
Key Insight
💡 Hierarchical robustness can improve vision-language model performance and reliability against adversarial attacks
Share This
Boost vision-language model robustness with hierarchical approaches! #AI #ComputerVision #Robustness
Full Article
Title: Hierarchically Robust Zero-shot Vision-language Models
Abstract:
arXiv:2604.18867v1 Announce Type: cross Abstract: Vision-Language Models (VLMs) can perform zero-shot classification but are susceptible to adversarial attacks. While robust fine-tuning improves their robustness, existing approaches align fixed text embeddings with an image embedding, sacrificing natural performance and robustness. A robustness degradation also occurs when a model faces adversarial attacks targeting superclasses (parent classes, e.g., mammal) in addition to their base (leaf) cla
Abstract:
arXiv:2604.18867v1 Announce Type: cross Abstract: Vision-Language Models (VLMs) can perform zero-shot classification but are susceptible to adversarial attacks. While robust fine-tuning improves their robustness, existing approaches align fixed text embeddings with an image embedding, sacrificing natural performance and robustness. A robustness degradation also occurs when a model faces adversarial attacks targeting superclasses (parent classes, e.g., mammal) in addition to their base (leaf) cla
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