IEEE

DINO-DIS: Vision Foundation Model-Guided Feature Disentanglement for Adverse Weather Adaptation

Abstract: Existing unsupervised domain adaptation (UDA) methods rely on labeled source data and unlabeled target data. However, in source-free unsupervised domain adaptation (SFUDA) scenarios, only a ...
GitHub

Sparse Autoencoders Learn Monosemantic Features in Vision-Language Models

Sparse Autoencoders (SAEs) have recently gained attention as a means to improve the interpretability and steerability of Large Language Models (LLMs), both of which are essential for AI safety. In ...
IEEE

Exploiting Shared Adversarial Features for Dynamic Attacks in Large Vision-Language Models

Abstract: With the rapid development of Large Language Models (LLMs), an increasing number of Large Visual-Language Models (LVLMs) have achieved unprecedented performance in response generation.