2 citations · 4 across the 13 of their papers we have counts for
6 papers · 1 filter
AIM: Anchor Identity Features, Then Match for Multimodal Large Language Model Unlearning
Wonjun Lee, Jaehyuk Jang, Kangwook Ko +2
Multimodal large language models (MLLMs) can memorize identity-specific facts about people in their fine-tuning data, creating privacy risks when a person requests deletion. Existi…
T-VSS: Test-Time Visual Subspace Steering for Adversarial Robustness of Vision-Language Models
Jaehyuk Jang, Minseok Seo, Seungju Cho +2
Vision-language models (VLMs) achieve strong zero-shot recognition, but they remain highly vulnerable to adversarial perturbations. Recent test-time adaptations improve robustness…
StreamTTO: Efficient Online Test-Time Optimization for Video Depth Completion
Minseok Seo, Wonjun Lee, Jaehyuk Jang +1
Monocular depth foundation models generalize across diverse scenes, but recovering accurate metric depth consistent with a target sensor remains challenging under sensor variation…
RITUAL: Random Image Transformations as a Universal Anti-hallucination Lever in Large Vision Language Models
Sangmin Woo, Jaehyuk Jang, Donguk Kim +2
Recent advancements in Large Vision Language Models (LVLMs) have revolutionized how machines understand and generate textual responses based on visual inputs, yet they often produc…
Don't Miss the Forest for the Trees: Attentional Vision Calibration for Large Vision Language Models
Sangmin Woo, Donguk Kim, Jaehyuk Jang +2
Large Vision Language Models (LVLMs) demonstrate strong capabilities in visual understanding and description, yet often suffer from hallucinations, attributing incorrect or mislead…
Towards Robust Multimodal Prompting With Missing Modalities
Jaehyuk Jang, Yooseung Wang, Changick Kim
Recently, multimodal prompting, which introduces learnable missing-aware prompts for all missing modality cases, has exhibited impressive performance. However, it encounters two cr…