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Changsheng Lu [CV-English, CV-中文] received the M.S. degree in control science and engineering from Shanghai Jiao Tong University, Shanghai, China, in 2020, and received the B.S. degree in Automation from Southeast University, Nanjing, China, in 2017. Currently, he is a PhD student with the College of Engineering and Computer Science at The Australian National University, and works with his supervisor Dr. Piotr Koniusz. He has wide research interests mainly including computer vision, image processing, few-shot learning, transfer learning, pattern recognition, and robotics. Particularly, he is interested in the theories and algorithms that may improve a robot or an intelligent device’s peception, cognition and reasoning ability, which could empower machine to see, think and conduct more like a human.

Previously, he was awarded the national scholarship for graduate student, and listed in the first term of Huawei F(X) future scientist program member and the outstanding undergraduate of Southeast University. He had a summer internship in Huawei Nanjing Research Institute in 2016. From Oct. 20, 2016, to May 1, 2017, he served as a part-time duty personnel in Joint Stars Technology CO., LTD, Nanjing, where he gained lots of knowledge about industrial standards and engineering implementation.

Mentorship & Collaboration: From times to times, I mentor students and am passionate to collaborate with people on amazing research projects/ideas. If you would like to collaborate with me, don’t hesitate to drop me an email.

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News

Our paper ``Detect Any Keypoints: An Efficient Light-Weight Few-Shot Keypoint Detector’’ has been accepted to AAAI (the 38th Annual AAAI Conference on Artificial Intelligence)!
One co-first authored paper about Few-shot shape recognition (FSSD) has been accepted to WACV’24. To our best knowledge, this is the first work in the field.
Two co-authored papers (Re. Diatom object detection & Shadow removal) have been accepted to IJCNN’23.
One research paper about Few-shot Keypoint Detection (FSKD) gets accepted by CVPR’22.
Two co-authored papers get accepted by ACCV’22 (Re. Deep General Ellipse detection) and ICONIP’22 (Re. Fast Stain Color Normalization). One co-authored paper about industrial scene text detection gets accepted by IEEE TCSVT.
Two papers about domain adaptation/transfer learning published in ICONIP’18 and Neurocomputing. [here]
Our High-quality Ellipse Detector gets accepted by IEEE T-IP and exceeds 300+ stars in github (rank top1 so far). We also have an IEEE ICIP version for circle detection.