1 citations · 1 across the 6 of their papers we have counts for
7 papers
CORA: A Protocol for Diagnosing Boundary Robustness in Text-to-Audio Retrieval under Query Reformulations
Jae Min Woo, Kyongmin Kong, Bogyung Jeong +3
Text-to-Audio (T2A) retrievers are typically evaluated with caption style queries, but the same user intent can be expressed in many forms. We introduce CORA (Caption-Offset Retrie…
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-…
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…
BI-MDRG: Bridging Image History in Multimodal Dialogue Response Generation
Hee Suk Yoon, Eunseop Yoon, Joshua Tian Jin Tee +4
Multimodal Dialogue Response Generation (MDRG) is a recently proposed task where the model needs to generate responses in texts, images, or a blend of both based on the dialogue co…
Solution for SMART-101 Challenge of CVPR Multi-modal Algorithmic Reasoning Task 2024
Jinwoo Ahn, Junhyeok Park, Min-Jun Kim +6
In this paper, the solution of HYU MLLAB KT Team to the Multimodal Algorithmic Reasoning Task: SMART-101 CVPR 2024 Challenge is presented. Beyond conventional visual question-answe…
Translation Deserves Better: Analyzing Translation Artifacts in Cross-lingual Visual Question Answering
ChaeHun Park, Koanho Lee, Hyesu Lim +5
Building a reliable visual question answering~(VQA) system across different languages is a challenging problem, primarily due to the lack of abundant samples for training. To addre…
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…