2 citations · 4 across the 13 of their papers we have counts for
13 papers
Where Identity Lives: Localized, Retain-Free Identity Unlearning in Multimodal Large Language Models
Kangwook Ko, Jaehyuk Jang, Wonjun Lee +2
Removing a specific individual's information from multimodal large language models (MLLMs) is often needed after deployment, but existing methods rely on a retain set, which is har…
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…
Acoustic Prompting via Stage-wise Modulation for Few-Shot Learning in Audio Language Models
Hyebin Cho, Jaehyuk Jang, Changick Kim +1
Audio-Language Models (ALMs) have shown remarkable success in zero-shot audio classification by aligning audio waveforms with text. Recent efforts to improve downstream performance…
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…
Who Wins the Conflict? Mechanistic Interpretability of Text Bias in Audio LLMs
Hyebin Cho, Suho Yoo, Jaehyuk Jang +2
While Audio Large Language Models (Audio LLMs) excel at multimodal understanding, they suffer from text dominance, a bias where models favor text over acoustic evidence, potentiall…
SubT: Subspace Tuning for Few-shot Generalization of Audio-Language Models
Jaehyuk Jang, Kangwook Ko, Wonjun Lee +1
Few-shot parameter-efficient adaptation of pretrained Audio--Language Models (ALMs) often improves seen-class performance at the cost of unseen-class generalization, leading to the…
Jailbreak to Protect: Buffering and Reinforcing via Temporary Jailbreaking for Safe Fine-Tuning in Large Language Models
Seokil Ham, Jaehyuk Jang, Wonjun Lee +1
Fine-tuning-as-a-Service (FaaS) enables personalization of large language models (LLMs), but it can weaken safety-alignment under harmful fine-tuning attacks. Recent work has shown…
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…
Multimodal Self-Attention Network with Temporal Alignment for Audio-Visual Emotion Recognition
Inyong Koo, yeeun Seong, Minseok Son +2
Audio-visual emotion recognition (AVER) methods typically fuse utterance-level features, and even frame-level attention models seldom address the frame-rate mismatch across modalit…
Generalizable Prompt Tuning for Audio-Language Models via Semantic Expansion
Jaehyuk Jang, Wonjun Lee, Kangwook Ko +1
Prompt tuning has achieved remarkable progress in vision-language models (VLMs) and is recently being adopted for audio-language models (ALMs). However, its generalization ability…
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…