Difference between revisions of "Daesik Kim"
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| <font size = 5> '''[[Daesik Kim]] (김대식)'''</font><br><br> | | <font size = 5> '''[[Daesik Kim]] (김대식)'''</font><br><br> | ||
− | :서울대학교 융합과학부 석박사통합과정 (2014.09 ~ ) <br> | + | :서울대학교 융합과학부 석박사통합과정 졸업 (2014.09 ~ 2019.08) <br> |
:포항공과대학교 산업경영공학과 졸업 (2007.08) <br> | :포항공과대학교 산업경영공학과 졸업 (2007.08) <br> | ||
<br> | <br> | ||
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https://scholar.google.com/citations?user=YUcWWbEAAAAJ&hl=en | https://scholar.google.com/citations?user=YUcWWbEAAAAJ&hl=en | ||
− | * Seo, J., Kim, D., Suh, B., & Lee, J. (2015, April). Design of a Smart TV Logging System Using Beacons and Smartphones | + | * Seo, J., Kim, D., Suh, B., & Lee, J. (2015, April). "[https://dl.acm.org/citation.cfm?id=2732858&dl=ACM&coll=DL Design of a Smart TV Logging System Using Beacons and Smartphones]", In Proceedings of the 33rd Annual ACM Conference Extended Abstracts on Human Factors in Computing Systems (pp. 2157-2162). ACM. |
− | * | + | * Seonhoon Kim, Daesik Kim and Bongwon Suh "[https://dl.acm.org/citation.cfm?id=2903686&dl=ACM&coll=DL Music Genre Classification using Multimodal Deep Learning]" Proceedings of HCI Korea '16 |
− | * Daesik Kim, Myunggi Lee and Nojun Kwak, "[http://ieeexplore.ieee.org/document/7965886/ Matching Video Net: Memory-based embedding for video action recognition | + | * Daesik Kim, Myunggi Lee and Nojun Kwak, "[http://ieeexplore.ieee.org/document/7965886/ Matching Video Net: Memory-based embedding for video action recognition]", International Joint Conference on Neural Networks 2017 (IJCNN2017), Anchorage, AK, May 2017. |
* Sangkuk Lee, Daesik Kim, Myunggi Lee, Jihye Hwang and Nojun Kwak, "[https://arxiv.org/pdf/1707.00251.pdf Where to Play: Retrieval of video segments using natural-language queries]", arXiv, July 2017. | * Sangkuk Lee, Daesik Kim, Myunggi Lee, Jihye Hwang and Nojun Kwak, "[https://arxiv.org/pdf/1707.00251.pdf Where to Play: Retrieval of video segments using natural-language queries]", arXiv, July 2017. |
Latest revision as of 14:45, 21 October 2019
Daesik Kim (김대식)
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Research Interests
- Machine Learning, Multi-modal Deep Learning, Knowledge based Deep learning
Education
- B.Sc. Industrial Engineering and Management, Pohang University of Science and Technology, 2007
Employment
- System developer, Programmer(Powerbuilder, Oracle), Mega IT. 2002-2005
- Assistant Manager ,Tresury department, Kookmin bank, 2007-2014
- Founder & CEO/CTO in V.DO, 2017~
Publication
https://scholar.google.com/citations?user=YUcWWbEAAAAJ&hl=en
- Seo, J., Kim, D., Suh, B., & Lee, J. (2015, April). "Design of a Smart TV Logging System Using Beacons and Smartphones", In Proceedings of the 33rd Annual ACM Conference Extended Abstracts on Human Factors in Computing Systems (pp. 2157-2162). ACM.
- Seonhoon Kim, Daesik Kim and Bongwon Suh "Music Genre Classification using Multimodal Deep Learning" Proceedings of HCI Korea '16
- Daesik Kim, Myunggi Lee and Nojun Kwak, "Matching Video Net: Memory-based embedding for video action recognition", International Joint Conference on Neural Networks 2017 (IJCNN2017), Anchorage, AK, May 2017.
- Sangkuk Lee, Daesik Kim, Myunggi Lee, Jihye Hwang and Nojun Kwak, "Where to Play: Retrieval of video segments using natural-language queries", arXiv, July 2017.
- Daesik Kim, Youngjoon Yoo, Jeesoo Kim, Sangkuk Lee and Nojun Kwak, "Dynamic Graph Generation Network: Generating Relational Knowledge from Diagrams", The Thirtieth IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR2018), accepted.
- Daesik Kim, Seonhoon Kim, and Nojun Kwak, "Textbook Question Answering with Multi-modal Context Graph Understanding and Self-supervised Open-set Comprehension", 57th Annual Meeting of the Association for Computational Linguistics (ACL2019), Florence, Italy. July 2019 (accepted, arXiv).
Competition
- 4th place of "Textbook Question Answering Challenge", CVPR 2017 Workshop on Visual Understanding Across Modalities
Teaching
- Tensorflow tutorial at OSIA (Aug. 2016)
- Tensorflow tutorial at CONTECH (Apr. 2017)
- Deeplearning class for artists at NABI artcenter (oct.~ dec. 2017, 8 weeks)