2 citations · 3 across the 5 of their papers we have counts for
6 papers
Noise Adaptive Streaming Audio-Visual Speech Token Enhancement for Robust Full-Duplex Spoken Dialogue Models
Bella Godiva, Yeonju Kim, Yong Man Ro
Full-duplex spoken dialogue systems enable simultaneous listening and speaking, but their audio-only perception often fails under background noise and overlapping speech, leading t…
Emotion-Coherent Reasoning for Multimodal LLMs via Emotional Rationale Verifier
Hyeongseop Rha, Jeong Hun Yeo, Yeonju Kim +1
The recent advancement of Multimodal Large Language Models (MLLMs) is transforming human-computer interaction (HCI) from surface-level exchanges into more nuanced and emotionally i…
Empathetic Response in Audio-Visual Conversations Using Emotion Preference Optimization and MambaCompressor
Yeonju Kim, Se Jin Park, Yong Man Ro
Chatbot research is advancing with the growing importance of chatbots in fields that require human interactions, such as customer support and mental health care. Despite these adva…
AV-EmoDialog: Chat with Audio-Visual Users Leveraging Emotional Cues
Se Jin Park, Yeonju Kim, Hyeongseop Rha +2
In human communication, both verbal and non-verbal cues play a crucial role in conveying emotions, intentions, and meaning beyond words alone. These non-linguistic information, suc…
CODE: Contrasting Self-generated Description to Combat Hallucination in Large Multi-modal Models
Junho Kim, Hyunjun Kim, Yeonju Kim +1
Large Multi-modal Models (LMMs) have recently demonstrated remarkable abilities in visual context understanding and coherent response generation. However, alongside these advanceme…
What if...?: Thinking Counterfactual Keywords Helps to Mitigate Hallucination in Large Multi-modal Models
Junho Kim, Yeon Ju Kim, Yong Man Ro
This paper presents a way of enhancing the reliability of Large Multi-modal Models (LMMs) in addressing hallucination, where the models generate cross-modal inconsistent responses.…