1 citations · 2 across the 11 of their papers we have counts for
4 papers · 1 filter
Keep What Audio Cannot Say: Context-Preserving Token Pruning for Omni-LLMs
Chaeyoung Jung, Kyeongha Rho, Joon Son Chung
Omnimodal Large Language Models (Omni-LLMs) incur substantial computational overhead due to the large number of multimodal input tokens they process, making token reduction essenti…
Fork-Merge Decoding: Enhancing Multimodal Understanding in Audio-Visual Large Language Models
Chaeyoung Jung, Youngjoon Jang, Jongmin Choi +1
The goal of this work is to enhance balanced multimodal understanding in audio-visual large language models (AV-LLMs) by addressing modality bias without additional training. In cu…
AVCD: Mitigating Hallucinations in Audio-Visual Large Language Models through Contrastive Decoding
Chaeyoung Jung, Youngjoon Jang, Joon Son Chung
Hallucination remains a major challenge in multimodal large language models (MLLMs). To address this, various contrastive decoding (CD) methods have been proposed that contrasts or…
TalkNCE: Improving Active Speaker Detection with Talk-Aware Contrastive Learning
Chaeyoung Jung, Suyeon Lee, Kihyun Nam +4
The goal of this work is Active Speaker Detection (ASD), a task to determine whether a person is speaking or not in a series of video frames. Previous works have dealt with the tas…