1 citations · 1 across the 12 of their papers we have counts for
15 papers
ImpactMat: Continuous Material Estimation for Inverse Impact Sound Rendering
Hyebin Cho, Bumsoo Kim, Joon Son Chung +1
Impact sound rendering synthesizes the sound produced when a 3D object is struck, but practical renderers often rely on fixed material presets such as wood, plastic, or steel. Thes…
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…
ProsoCodec: Prosody-Oriented Speech Codec for Voice Conversion
Jeongsoo Choi, Ji-Hoon Kim, Shujie Hu +1
Neural speech codecs efficiently compress speech and have become a foundation for speech generation, but they are typically learned as holistic representations that intertwine ling…
Acoustic Prompting via Stage-wise Modulation for Few-Shot Learning in Audio Language Models
Hyebin Cho, Jaehyuk Jang, Changick Kim +1
Audio-Language Models (ALMs) have shown remarkable success in zero-shot audio classification by aligning audio waveforms with text. Recent efforts to improve downstream performance…
Inference-Time Scaling for Joint Audio-Video Generation
Jaemin Jung, Kyeongha Rho, Inkyu Shin +1
Joint audio-video generation aims to synthesize realistic audio-video pairs that are both semantically aligned with text prompts and precisely synchronized. While existing joint au…
Who Wins the Conflict? Mechanistic Interpretability of Text Bias in Audio LLMs
Hyebin Cho, Suho Yoo, Jaehyuk Jang +2
While Audio Large Language Models (Audio LLMs) excel at multimodal understanding, they suffer from text dominance, a bias where models favor text over acoustic evidence, potentiall…
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…
LAMB: LLM-based Audio Captioning with Modality Gap Bridging via Cauchy-Schwarz Divergence
Hyeongkeun Lee, Jongmin Choi, KiHyun Nam +1
Automated Audio Captioning aims to describe the semantic content of input audio. Recent works have employed large language models (LLMs) as a text decoder to leverage their reasoni…
MoLT: Mixture of Layer-Wise Tokens for Efficient Audio-Visual Learning
Kyeongha Rho, Hyeongkeun Lee, Jae Won Cho +1
In this paper, we propose Mixture of Layer-Wise Tokens (MoLT), a parameter- and memory-efficient adaptation framework for audio-visual learning. The key idea of MoLT is to replace…
Diffusion-Link: Diffusion Probabilistic Model for Bridging the Audio-Text Modality Gap
KiHyun Nam, Jongmin Choi, Hyeongkeun Lee +2
Contrastive audio-language pretraining yields powerful joint representations, yet a persistent audio-text modality gap limits the benefits of coupling multimodal encoders with larg…
LAVCap: LLM-based Audio-Visual Captioning using Optimal Transport
Kyeongha Rho, Hyeongkeun Lee, Valentio Iverson +1
Automated audio captioning is a task that generates textual descriptions for audio content, and recent studies have explored using visual information to enhance captioning quality.…
EquiAV: Leveraging Equivariance for Audio-Visual Contrastive Learning
Jongsuk Kim, Hyeongkeun Lee, Kyeongha Rho +2
Recent advancements in self-supervised audio-visual representation learning have demonstrated its potential to capture rich and comprehensive representations. However, despite the…