3 papers
cs.SD2026
Omni-Embed-Audio: Leveraging Multimodal LLMs for Robust Audio-Text Retrieval
HaeJun Yoo, Yongseop Shin, Insung Lee +2
Audio-text retrieval systems based on Contrastive Language-Audio Pretraining (CLAP) achieve strong performance on traditional benchmarks; however, these benchmarks rely on caption-…
cs.CV2025
Instruction-tuned Self-Questioning Framework for Multimodal Reasoning
You-Won Jang, Yu-Jung Heo, Jaeseok Kim +3
The field of vision-language understanding has been actively researched in recent years, thanks to the development of Large Language Models~(LLMs). However, it still needs help wit…
cs.CL2025
Evaluating Visual and Cultural Interpretation: The K-Viscuit Benchmark with Human-VLM Collaboration
ChaeHun Park, Yujin Baek, Jaeseok Kim +3
To create culturally inclusive vision-language models (VLMs), developing a benchmark that tests their ability to address culturally relevant questions is essential. Existing approa…