4 citations · 6 across the 12 of their papers we have counts for
4 papers · 1 filter
Token-level Response-visual Attention Guidance for Multimodal LLMs Knowledge Distillation
Jaehyun Jang, Eunseop Yoon, Hee Suk Yoon +3
While knowledge distillation (KD) is widely adopted for training lightweight models by leveraging supervision from larger teacher models, relying solely on output token distributio…
Decomposed On-Policy Distillation for Vision-Language Reasoning: Steering Gradients for Visual Grounding
Hee Suk Yoon, Eunseop Yoon, Jaehyun Jang +6
While on-policy distillation offers dense supervision for training small reasoning models, its optimization dynamics in the multimodal domain remain under-explored. In this work, w…
Can Video LLMs Refuse to Answer? Alignment for Answerability in Video Large Language Models
Eunseop Yoon, Hee Suk Yoon, Mark A. Hasegawa-Johnson +1
In the broader context of deep learning, Multimodal Large Language Models have achieved significant breakthroughs by leveraging powerful Large Language Models as a backbone to alig…
C-TPT: Calibrated Test-Time Prompt Tuning for Vision-Language Models via Text Feature Dispersion
Hee Suk Yoon, Eunseop Yoon, Joshua Tian Jin Tee +3
In deep learning, test-time adaptation has gained attention as a method for model fine-tuning without the need for labeled data. A prime exemplification is the recently proposed te…