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研
人 Research Fellow
員 劉庭祿 Tyng-Luh Liu
Faculty Ph.D., Computer Science, New York University, United States
T +886-2-2788-3799 ext.1508 E liutyng@iis.sinica.edu.tw
F +886-2-2782-4814 W www.iis.sinica.edu.tw/pages/liutyng
・ Chief Scientist, Taiwan AI Labs (2019-present)
・ Academia Sinica Young Investigator Award (2006)
・ Visiting Scholar, School of Electrical and Computer Engineering, Cornell University (2013-
2014)
Research Description
My research has focused on computer vision and machine learning techniques that support real-life applications. I am most interested in
understanding the fundamentals, and addressing critical issues in realizing vision (particularly, scene understanding) and natural language
processing. Since the aforementioned applications rely heavily on the underlying imaging devices such as RGBD, 360-degree cameras, my
research e orts also take this perspective into account in designing computer vision algorithms, ranging from low-level to high-level, to more
appropriately exploit the available imaging information about the scene and its contents. In addition, generalizing conventional computer
vision methods to deep learning approaches will continue to play a major role of my research themes. To expand my research scope, I
collaborate with domain experts in designing e ective methods for medical imaging.
Publications
1. Yen-Chi Hsu, Cheng-Yao Hong, Ming-Sui Lee, and Tyng-Luh 6. Kai-Yueh Chang, Tyng-Luh Liu, Hwann-Tzong Chen, and Shang-
Liu, "Query-Driven Multi-Instance Learning," Thirty-Fourth Hong Lai, "Fusing Generic Objectness and Visual Saliency for
AAAI Conference on Artificial Intelligence, February 2020. Salient Object Detection," International Conference on Computer
(AAAI) Vision, Barcelona, Spain, November 2011. (ICCV)
2. Ting-I Hsieh, Yi-Chen Lo, Hwann-Tzong Chen, and Tyng-Luh 7. Yen-Yu Lin, Tyng-Luh Liu, and Chiou-Shann Fuh, "Multiple
Liu, "One-Shot Object Detection with Co-Attention and Co- Kernel Learning for Dimensionality Reduction," IEEE
Excitation," Thirty-third Conference on Neural Information Transactions on Pattern Analysis and Machine Intelligence, vol.
Processing Systems, December 2019. (NeurIPS) 33, no. 6, pp. 1147-1160, 2011. (TPAMI)
3. Ding-Jie Chen, Songhao Jia, Yi-Chen Lo, Hwann-Tzong Chen, 8. Ye n - Yu L i n , Ty n g - L u h L i u , a n d C h i o u - S h a n n F u h ,
and Tyng-Luh Liu, "See-through-Text Grouping for Referring "Dimensionality Reduction for Data in Multiple Feature
Image Segmentation," International Conference on Computer Representations," Twenty-second Conference on Neural
Vision, October 2019. (ICCV) Information Processing Systems, December 2008. (NeurIPS)
4. Ding-Jie Chen, Jui-Ting Chien, Hwann-Tzong Chen, and Tyng- 9. Yen-Yu Lin, Tyng-Luh Liu, and Chiou-Shann Fuh, "Local
Luh Liu, "Unsupervised Meta-learning of Figure-Ground Ensemble Kernel Learning for Object Category Recognition,"
Segmentation via Imitating Visual Effects," Thirty-Third AAAI IEEE Computer Society International Conference on Computer
Conference on Artificial Intelligence, January 2019. (AAAI) Vision and Pattern Recognition, Minneapolis, MN, USA, June
2007. (CVPR)
5. Hsien-Tzu Cheng, Chun-Hung Chao, Jin-Dong Dong, Hao-Kai
Wen, Tyng-Luh Liu, and Min Sun, "Cube Padding for Weakly- 10. Hwann-Tzong Chen, Huang-Wei Chang, and Tyng-Luh Liu,
Supervised Saliency Prediction in 360° Videos," IEEE Computer "Local Discriminant Embedding and Its Variants," IEEE
Society International Conference on Computer Vision and Pattern Computer Society International Conference on Computer Vision
Recognition, Salt Lake City, Utah, June 2018. (CVPR) and Pattern Recognition, vol. 1, pp. 679-686, San Diego, CA,
USA, June 2005. (CVPR)
180
人 Research Fellow
員 劉庭祿 Tyng-Luh Liu
Faculty Ph.D., Computer Science, New York University, United States
T +886-2-2788-3799 ext.1508 E liutyng@iis.sinica.edu.tw
F +886-2-2782-4814 W www.iis.sinica.edu.tw/pages/liutyng
・ Chief Scientist, Taiwan AI Labs (2019-present)
・ Academia Sinica Young Investigator Award (2006)
・ Visiting Scholar, School of Electrical and Computer Engineering, Cornell University (2013-
2014)
Research Description
My research has focused on computer vision and machine learning techniques that support real-life applications. I am most interested in
understanding the fundamentals, and addressing critical issues in realizing vision (particularly, scene understanding) and natural language
processing. Since the aforementioned applications rely heavily on the underlying imaging devices such as RGBD, 360-degree cameras, my
research e orts also take this perspective into account in designing computer vision algorithms, ranging from low-level to high-level, to more
appropriately exploit the available imaging information about the scene and its contents. In addition, generalizing conventional computer
vision methods to deep learning approaches will continue to play a major role of my research themes. To expand my research scope, I
collaborate with domain experts in designing e ective methods for medical imaging.
Publications
1. Yen-Chi Hsu, Cheng-Yao Hong, Ming-Sui Lee, and Tyng-Luh 6. Kai-Yueh Chang, Tyng-Luh Liu, Hwann-Tzong Chen, and Shang-
Liu, "Query-Driven Multi-Instance Learning," Thirty-Fourth Hong Lai, "Fusing Generic Objectness and Visual Saliency for
AAAI Conference on Artificial Intelligence, February 2020. Salient Object Detection," International Conference on Computer
(AAAI) Vision, Barcelona, Spain, November 2011. (ICCV)
2. Ting-I Hsieh, Yi-Chen Lo, Hwann-Tzong Chen, and Tyng-Luh 7. Yen-Yu Lin, Tyng-Luh Liu, and Chiou-Shann Fuh, "Multiple
Liu, "One-Shot Object Detection with Co-Attention and Co- Kernel Learning for Dimensionality Reduction," IEEE
Excitation," Thirty-third Conference on Neural Information Transactions on Pattern Analysis and Machine Intelligence, vol.
Processing Systems, December 2019. (NeurIPS) 33, no. 6, pp. 1147-1160, 2011. (TPAMI)
3. Ding-Jie Chen, Songhao Jia, Yi-Chen Lo, Hwann-Tzong Chen, 8. Ye n - Yu L i n , Ty n g - L u h L i u , a n d C h i o u - S h a n n F u h ,
and Tyng-Luh Liu, "See-through-Text Grouping for Referring "Dimensionality Reduction for Data in Multiple Feature
Image Segmentation," International Conference on Computer Representations," Twenty-second Conference on Neural
Vision, October 2019. (ICCV) Information Processing Systems, December 2008. (NeurIPS)
4. Ding-Jie Chen, Jui-Ting Chien, Hwann-Tzong Chen, and Tyng- 9. Yen-Yu Lin, Tyng-Luh Liu, and Chiou-Shann Fuh, "Local
Luh Liu, "Unsupervised Meta-learning of Figure-Ground Ensemble Kernel Learning for Object Category Recognition,"
Segmentation via Imitating Visual Effects," Thirty-Third AAAI IEEE Computer Society International Conference on Computer
Conference on Artificial Intelligence, January 2019. (AAAI) Vision and Pattern Recognition, Minneapolis, MN, USA, June
2007. (CVPR)
5. Hsien-Tzu Cheng, Chun-Hung Chao, Jin-Dong Dong, Hao-Kai
Wen, Tyng-Luh Liu, and Min Sun, "Cube Padding for Weakly- 10. Hwann-Tzong Chen, Huang-Wei Chang, and Tyng-Luh Liu,
Supervised Saliency Prediction in 360° Videos," IEEE Computer "Local Discriminant Embedding and Its Variants," IEEE
Society International Conference on Computer Vision and Pattern Computer Society International Conference on Computer Vision
Recognition, Salt Lake City, Utah, June 2018. (CVPR) and Pattern Recognition, vol. 1, pp. 679-686, San Diego, CA,
USA, June 2005. (CVPR)
180
