Summary of Sim-clip: Unsupervised Siamese Adversarial Fine-tuning For Robust and Semantically-rich Vision-language Models, by Md Zarif Hossain et al.
Sim-CLIP: Unsupervised Siamese Adversarial Fine-Tuning for Robust and Semantically-Rich Vision-Language Models
by Md Zarif Hossain, Ahmed Imteaj
First submitted to arxiv on: 20 Jul 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents a method called Sim-CLIP that enhances the robustness of vision-language models (VLMs) against adversarial attacks on their vision components. The approach uses an unsupervised fine-tuning method to improve the CLIP vision encoder’s ability to resist attacks while maintaining semantic richness and specificity. The proposed architecture, which employs a Siamese structure with cosine similarity loss, learns robust visual representations without requiring large batch sizes or momentum encoders. The results show that VLMs enhanced with Sim-CLIP’s fine-tuned CLIP encoder exhibit significantly improved robustness against adversarial attacks while preserving the semantic meaning of perturbed images. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps keep vision-language models safe from bad attacks! It proposes a way to make these models stronger by improving their “vision” part. The method, called Sim-CLIP, doesn’t need extra training and can be used with existing models. It makes sure the models stay good at understanding images even when they’re changed in ways that might try to trick them. |
Keywords
» Artificial intelligence » Cosine similarity » Encoder » Fine tuning » Unsupervised
