1 citations · 2 across the 9 of their papers we have counts for
6 papers · 1 filter
Visual Grounding First, Multimodal In-context Learning Follows
Minhyuk Seo, Minjae Lee, Chaeeun Lee +4
LLMs have shown strong in-context learning (ICL) capability, but extending it to Multimodal LLMs (MLLMs) remains challenging. Prior multimodal ICL methods often rely on ICL-specifi…
Attention Misses Visual Risk: Risk-Adaptive Steering for Multimodal Safety Alignment
Jonghyun Park, Minhyuk Seo, Chaewon Yeo +1
Even modern AI models often remain vulnerable to multimodal queries in which harmful intent is embedded in images. A widely used approach for safety alignment is training with exte…
Multi-Level Knowledge Distillation and Dynamic Self-Supervised Learning for Continual Learning
Taeheon Kim, San Kim, Minhyuk Seo +3
Class-incremental with repetition (CIR), where previously trained classes repeatedly introduced in future tasks, is a more realistic scenario than the traditional class incremental…
TTA-DAME: Test-Time Adaptation with Domain Augmentation and Model Ensemble for Dynamic Driving Conditions
Dongjae Jeon, Taeheon Kim, Seongwon Cho +2
Test-time Adaptation (TTA) poses a challenge, requiring models to dynamically adapt and perform optimally on shifting target domains. This task is particularly emphasized in real-w…
OASIS: Online Sample Selection for Continual Visual Instruction Tuning
Minjae Lee, Minhyuk Seo, Tingyu Qu +2
In continual instruction tuning (CIT) scenarios, where new instruction tuning data continuously arrive in an online streaming manner, training delays from large-scale data signific…
Learning Equi-angular Representations for Online Continual Learning
Minhyuk Seo, Hyunseo Koh, Wonje Jeung +7
Online continual learning suffers from an underfitted solution due to insufficient training for prompt model update (e.g., single-epoch training). To address the challenge, we prop…