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cs.CV2024
Defending Multimodal Backdoored Models by Repulsive Visual Prompt Tuning
Zhifang Zhang, Shuo He, Haobo Wang +2
Multimodal contrastive learning models (e.g., CLIP) can learn high-quality representations from large-scale image-text datasets, while they exhibit significant vulnerabilities to b…
cs.CV2024
Tuning Vision-Language Models with Candidate Labels by Prompt Alignment
Zhifang Zhang, Yuwei Niu, Xin Liu +1
Vision-language models (VLMs) can learn high-quality representations from a large-scale training dataset of image-text pairs. Prompt learning is a popular approach to fine-tuning V…