Dr Hyung Jin Chang PhD

School of Computer Science
Lecturer in Computer Science

Contact details

Email: h.j.chang@bham.ac.uk

Address: The School of Computer Science
University of Birmingham
Edgbaston
Birmingham
B15 2TT
UK

Dr Hyung Jin Chang was appointed as a Lecturer in the School of Computer Science at the University of Birmingham in 2018. He received a PhD in machine learning and computer vision at Perception and Intelligence Lab, Seoul National University, in 2013. As a post-doctoral researcher, he worked at Imperial College London and was involved in several EU projects (EU FP7 GRANT 612139 and EU H2020 GRANT 643783) and industry projects (Samsung Electronics and Samsung GRO Grant).

For more information, please vist Dr Hyung Jin's Computer Science profile.

Qualifications

PhD in Seoul National University 2013
BSc in Seoul National University 2006

Biography

Hyung Jin Chang qualified with a BSc in Electrical and Computer Engineering from Seoul National University in 2006, and PhD in Electrical Engineering and Computer Science from Seoul National University in 2013. Before joining the University of Birmingham, he was a post-doctoral researcher at Imperial College London.

Research

Dr Hyung Jin Chang’s research combines multiple areas of artificial intelligence including computer vision, machine learning, robotics, and human-robot interaction. Recently, his research has focused on exploiting and making advances in robot vision, and learning techniques to move toward intelligent interaction human-robot interaction via visual data. In particular, he is interested in human-centred visual learning covering from estimating human hand, body pose, and gaze to understanding human action and internal states.

Publications

Recent publications

Article

Chang, HJ & Demiris, Y 2018, 'Highly articulated kinematic structure estimation combining motion and skeleton information', IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 40, no. 9, pp. 2165-2179. https://doi.org/10.1109/TPAMI.2017.2748579

Chang, HJ, Fischer, T, Petit, M, Zambelli, M & Demiris, Y 2017, 'Learning kinematic structure correspondences using multi-order similarities', IEEE Transactions on Pattern Analysis and Machine Intelligence . https://doi.org/10.1109/TPAMI.2017.2777486

Moulin-frier, C, Fischer, T, Petit, M, Pointeau, G, Puigbo, J, Pattacini, U, Low, SC, Camilleri, D, Nguyen, P, Hoffmann, M, Chang, HJ, Zambelli, M, Mealier, A, Damianou, A, Metta, G, Prescott, TJ, Demiris, Y, Dominey, PF & Verschure, PFMJ 2017, 'DAC-h3: A proactive robot cognitive architecture to acquire and express knowledge about the world and the self', IEEE Transactions on Cognitive and Developmental Systems. https://doi.org/10.1109/TCDS.2017.2754143

Conference contribution

Park, J, Lee, M & Chang, HJ 2019, Symmetric graph convolutional autoencoder for unsupervised graph representation learning. in Proceedings of the IEEE International Conference on Computer Vision (ICCV 2019). IEEE Computer Society, IEEE International Conference on Computer Vision (ICCV 2019), Seoul, Korea, Democratic People's Republic of, 27/10/19.

Lim, J, Chang, HJ & Choi, JY 2019, PMnet: learning of disentangled pose and movement for unsupervised motion retargeting. in Proceedings of the 30th British Machine Vision Conference (BMVC 2019). British Machine Vision Association, BMVA, 30th British Machine Vision Conference (BMVC 2019), Cardiff, United Kingdom, 9/09/19.

Kim, KI & Chang, HJ 2019, Joint Manifold Diffusion for Combining Predictions on Decoupled Observations. in 2019 Conference on Computer Vision and Pattern Recognition (CVPR). Springer, pp. 7549-7557, 2019 Conference on Computer Vision and Pattern Recognition (CVPR 2019), Long Beach, CA, United States, 16/06/19.

Fischer, T, Chang, HJ & Demiris, Y 2018, RT-GENE: Real-Time Eye Gaze Estimation in Natural Environments. in Computer Vision – ECCV 2018: 15th European Conference, Munich, Germany, September 8-14, 2018, Proceedings, Part XV. Lecture Notes in Computer Science, vol. 11219, Springer, pp. 334-352, The European Conference on Computer Vision (ECCV), 2018, Munich, Germany, 8/09/18. https://doi.org/10.1007/978-3-030-01267-0

Nguyen, PDH, Fischer, T, Chang, HJ, Pattacini, U, Metta, G & Demiris, Y 2018, Transferring visuomotor learning from simulation to the real world for manipulation tasks in a humanoid robot. in IEEE/RSJ Conference on Intelligent Robots and Systems (IROS 2018). IEEE Computer Society, IEEE/RSJ Conference on Intelligent Robots and Systems (IROS 2018), Madrid, Spain, 1/10/18.

Choi, J, Chang, HJ, Fischer, T, Yun, S, Lee, K, Jeong, J, Demiris, Y & Choi, JY 2018, Context-aware Deep Feature Compression for High-speed Visual Tracking. in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2018). IEEE Computer Society, IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2018), Salt Lake City, United States, 18/06/18.

Choi, J, Chang, HJ, Yun, S, Fischer, T, Demiris, Y & Choi, JY 2017, Attentional Correlation Filter Network for Adaptive Visual Tracking. in 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE Computer Society, pp. 4828-4837, 30th IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2017), Honolulu, Hawaii, United States, 21/07/17. https://doi.org/10.1109/CVPR.2017.513

Yoo, Y, Yun, S, Chang, HJ, Demiris, Y & Choi, JY 2017, Variational Autoencoded Regression: High Dimensional Regression of Visual Data on Complex Manifold. in 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE Computer Society, pp. 2943-2952, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 21/07/17. https://doi.org/10.1109/CVPR.2017.314

Gao, Y, Chang, HJ & Demiris, Y 2016, Iterative path optimisation for personalised dressing assistance using vision and force information. in Proceedings of the 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE Computer Society, pp. 4398-4403, 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2016), Daejeon, Korea, Republic of, 9/10/16. https://doi.org/10.1109/IROS.2016.7759647

Discussion paper

Wang, R, Cully, A, Chang, HJ & Demiris, Y 2017 'MAGAN: Margin Adaptation for Generative Adversarial Networks' CoRR (Computing Research Repository), Cornell University Library.

Paper

Zambelli, M, Fischer, T, Petit, M, Chang, HJ, Cully, A & Demiris, Y 2016, 'Towards Anchoring Self-Learned Representations to Those of Other Agents' Paper presented at Workshop on Bio-inspired Social Robot Learning in Home Scenarios, Daejeon, Korea, Republic of, 10/10/16 - 10/10/16, .

Patent

Han, JJ, Tang, D, Kim, TK, Han, SJ, Yoo, BI, Choi, CK, Tejani, A & Chang, HJ 2017, 깊이 영상을 이용하는 추정기 학습 방법 및 자세 추정 방법, Patent No. KR101758064000, IPC No. G06T 7/20 and G06T 7/00.

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