6 citations · 11 across the 20 of their papers we have counts for
9 papers · 1 filter
Out of Sight, Still in Mind: Token Compression for Omni-LLMs
Suho Yoo, Youngjoon Jang, Hyebin Cho +1
The goal of this paper is to reduce the input token cost of Omni-modal large language models (Omni-LLMs) at inference time. Omni-LLMs reason jointly over audio, video and text, but…
Lost in Translation, Found in Embeddings: Sign Language Translation and Alignment
Youngjoon Jang, Liliane Momeni, Zifan Jiang +3
Our aim is to develop a unified model for sign language understanding, that performs sign language translation (SLT) and sign-subtitle alignment (SSA). Together, these two tasks en…
Test-Time Augmentation for Pose-invariant Face Recognition
Jaemin Jung, Youngjoon Jang, Joon Son Chung
The goal of this paper is to enhance face recognition performance by augmenting head poses during the testing phase. Existing methods often rely on training on frontalised images o…
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…
Deep Understanding of Sign Language for Sign to Subtitle Alignment
Youngjoon Jang, Jeongsoo Choi, Junseok Ahn +1
The objective of this work is to align asynchronous subtitles in sign language videos with limited labelled data. To achieve this goal, we propose a novel framework with the follow…