MACHINE LEARNING, VISION & LANGUAGE LAB

기계 학습 비전 및 언어처리 랩

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기계 학습 비전 및 언어처리 랩

우리 연구실은 사람의 가장 효과적인 소통 수단인 시각, 자연어, 그리고 음성을 통해 사람의 지능을 이해하고 구현하는 기계 학습 모델을 연구하고 있습니다. 구체적으로는 멀티모달 학습, 생성 모델, 심층 학습을 연구하고 있고, 연구 주제들은 문자에서 영상 및 동영상 생성, Embodied AI, 멀미모달 대화 모델, 동영상 이해 및 QA 모델, 이해 가능한 인공지능 등이 있습니다.
Our lab aims to help understanding and implement human intelligence for most common communication media: vision, natural language, and speech. Since they are connected and correlated to each other, we work on developing effective and efficient machine learning models for multi-modalities.
In Machine learning, Vision & Language lab, we are interested in Machine Learning and applications to Computer Vision and Language Processing. Specifically, we work on Multimodal Learning, Generative Models, and Deep Learning and our research topics include (but not limited to) embodied AI, text-to-image generation, multi-modal conversational models, video understanding and question answering, and explainable AI.

Major research field

기계 학습 및 컴퓨터 비전, 언어처리 / Machine Learning and applications to Computer Vision and Language Processing

Desired field of research

멀티모달 학습, 생성 모델, 기계학습, 심층 학습 / Multimodal Learning, Generative Models, Machine Learning, and Deep Learning

Research Keywords and Topics

· 텍스트-이미지/비디오 생성
Text-to-image/video generation
· 멀티모달 대화 모델
Multi-modal conversational models
· Embodied AI
Embodied AI
· 비디오 이해 및 답변 생성 모델
Video understanding and question answering

Research Publications

· Taegyeong Lee, Soyeong Kwon and Taehwan Kim, Grid Diffusion Models for Text- to-Video Generation, IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 2024
· Taegyeong Lee, Jeonghun Kang, Hyeonyu Kim and Taehwan Kim, Generating Realistic Images from In-the-wild Sounds, IEEE/CVF International Conference on Computer Vision (ICCV), October 2023
· Taehwan Kim, Yisong Yue, Sarah Taylor and Iain Matthews, A Decision Tree Framework for Spatiotemporal Sequence Prediction, ACM Conference on Knowledge Discovery and Data Mining (KDD), August 2015
· Taehwan Kim, Greg Shakhnarovich and Karen Livescu, Fingerspelling Recognition with semi- Markov Conditional Random Fields, IEEE International Conference on Computer Vision (ICCV), December 2013
· Taehwan Kim, Greg Shakhnarovich, and Raquel Urtasun, Sparse Coding for Learning Interpretable Spatio-Temporal Primitives, Neural Information Processing Systems (NeurIPS), December 2010