6 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…