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cs.CV2024
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.LG2024
Parameter Efficient Mamba Tuning via Projector-targeted Diagonal-centric Linear Transformation
Seokil Ham, Hee-Seon Kim, Sangmin Woo +1
Despite the growing interest in Mamba architecture as a potential replacement for Transformer architecture, parameter-efficient fine-tuning (PEFT) approaches for Mamba remain large…
cs.CV2024
VideoMamba: Spatio-Temporal Selective State Space Model
Jinyoung Park, Hee-Seon Kim, Kangwook Ko +2
We introduce VideoMamba, a novel adaptation of the pure Mamba architecture, specifically designed for video recognition. Unlike transformers that rely on self-attention mechanisms…