1 citations · 1 across the 12 of their papers we have counts for
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Uncovering Ordinal-Matching Bias in Audio-Visual LLMs
Jihoo Jung, Youngjoon Jang, Hyebin Cho +2
This work aims to improve how audio-visual large language models (AVLLMs) associate speech with the correct visible speaker in multi-speaker scenes. We find that current AVLLMs fre…
OmniSmartHome: A Multimodal Reasoning Benchmark for Smart-Home Agents
Jihoo Jung, Suho Yoo, Jeongsoo Choi +5
Smart-home assistants are expected to handle diverse, realistic requests that arise in daily life. In such interactions, users often rely on the surrounding multimodal context-poin…
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…
See & Sniff: Learning Visuo-Olfactory Representations
Seongyu Kim, Seungwoo Lee, Hyeonggon Ryu +2
While modern multimodal models integrate vision with language, audio, or touch, olfaction remains largely unexplored due to the lack of paired visuo-olfactory data. We introduce Sm…
On the Nature of Attention Sink that Shapes Decoding Strategy in Omni-LLMs
Suho Yoo, Youngjoon Jang, Joon Son Chung
The goal of this paper is to strengthen the reasoning of Omnimodal Large Language Models (Omni-LLMs) at inference time, without additional training. These models jointly process vi…