1 citations · 1 across the 8 of their papers we have counts for
9 papers
Continuous Audio Thinking for Large Audio Language Models
Gyojin Han, Dong-Jae Lee, Changho Choi +2
Large audio language models (LALMs) have shown impressive capabilities on diverse audio understanding tasks, ranging from speech transcription to music analysis. However, because L…
Learning Question-Aware Keyframe Selection with Synthetic Supervision for Video Question Answering
Minchan Kwon, Hyounguk Shon, Junmo Kim
Large multimodal models (LMMs) have recently demonstrated remarkable performance in video question answering (VideoQA), yet reasoning over video remains challenging due to high inf…
FxSearcher: gradient-free text-driven audio transformation
Hojoon Ki, Jongsuk Kim, Minchan Kwon +1
Achieving diverse and high-quality audio transformations from text prompts remains challenging, as existing methods are fundamentally constrained by their reliance on a limited set…
Preference Distillation via Value based Reinforcement Learning
Minchan Kwon, Junwon Ko, Kangil Kim +1
Direct Preference Optimization (DPO) is a powerful paradigm to align language models with human preferences using pairwise comparisons. However, its binary win-or-loss supervision…
FairASR: Fair Audio Contrastive Learning for Automatic Speech Recognition
Jongsuk Kim, Jaemyung Yu, Minchan Kwon +1
Large-scale ASR models have achieved remarkable gains in accuracy and robustness. However, fairness issues remain largely unaddressed despite their critical importance in real-worl…
InfiniteAudio: Infinite-Length Audio Generation with Consistency
Chaeyoung Jung, Hojoon Ki, Ji-Hoon Kim +2
This paper presents InfiniteAudio, a simple yet effective strategy for generating infinite-length audio using diffusion-based text-to-audio methods. Current approaches face memory…
StablePrompt: Automatic Prompt Tuning using Reinforcement Learning for Large Language Models
Minchan Kwon, Gaeun Kim, Jongsuk Kim +2
Finding appropriate prompts for the specific task has become an important issue as the usage of Large Language Models (LLM) has expanded. Reinforcement Learning (RL) is widely used…
AVCap: Leveraging Audio-Visual Features as Text Tokens for Captioning
Jongsuk Kim, Jiwon Shin, Junmo Kim
In recent years, advancements in representation learning and language models have propelled Automated Captioning (AC) to new heights, enabling the generation of human-level descrip…
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…