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cs.CV2026
Exploiting Vision Encoder Vulnerabilities for Universal Adversarial Perturbations on Large Vision-Language Models
Hee-Seon Kim, Minbeom Kim, Seokil Ham +1
Large Vision-Language Models (LVLMs) have achieved remarkable performance on multimodal tasks but remain highly vulnerable to small adversarial perturbations in input images. Exist…
cs.CV2024
Diffusion Model Patching via Mixture-of-Prompts
Seokil Ham, Sangmin Woo, Jin-Young Kim +3
We present Diffusion Model Patching (DMP), a simple method to boost the performance of pre-trained diffusion models that have already reached convergence, with a negligible increas…
cs.CV2024
Switch Diffusion Transformer: Synergizing Denoising Tasks with Sparse Mixture-of-Experts
Byeongjun Park, Hyojun Go, Jin-Young Kim +3
Diffusion models have achieved remarkable success across a range of generative tasks. Recent efforts to enhance diffusion model architectures have reimagined them as a form of mult…