Papers by Jinkyu Lee
Watermarking for Factuality: Guiding Vision-Language Models Toward Truth via Tri-layer Contrastive Decoding (2025.findings-emnlp)
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Kyungryul Back, Seongbeom Park, Milim Kim, Mincheol Kwon, SangHyeok Lee, Hyunyoung Lee, Junhee Cho, Seunghyun Park, Jinkyu Kim
| Challenge: | Large Vision-Language Models (LVLMs) have shown promising results on multimodal tasks, but remain prone to hallucinations due to their reliance on a single modality or memorizing training data without properly grounding their outputs. |
| Approach: | They propose a training-free, tri-layer contrastive decoding with watermarking that uses a watermark-related question to identify a pivot layer and apply tri-layered contrastive coding to generate the final output. |
| Outcome: | The proposed method reduces hallucinations and generates more visually grounded responses. |
FC-TTS: Style and Timbre Control in Zero-Shot Text-to-Speech with Disentangled Speech Representations (2026.acl-long)
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| Challenge: | Recent advances in text-to-speech (TTS) have enabled accurate imitation of reference speech in terms of both speaking style and speaker timbre. |
| Approach: | They propose a zero-shot text-to-speech framework that enables disentangled control of style and timbre by conditioning on two distinct reference utterances. |
| Outcome: | The proposed framework achieves high-fidelity synthesis and competitive zero-shot naturalness while supporting consistent and independent manipulation of style and timbre. |