Announcement: ACM 2025 Recruitment for Internship / Master / Ph.D. program / Full-time researcher is open! Join us
ACM Lab - National Yang Ming Chiao Tung University
Our lab works on a wide range of projects in computer vision and multimedia. Currently, we employ advanced machine learning techniques such as deep learning to develop our systems.
Some of our applications include museum guidance, autonomous vehicle, parking lot management system … Most of them are confirmed and supported by industry. Through the process of completing the projects, the student will be equipped the self-studying ability as well as the teamwork skill to adapt to an ever-changing world.
Besides, we also try to build an international environment in our lab, you may find that our lab members come from many countries such as Taiwan, Vietnam and Indonesia. Therefore, working in our lab, you also can experience different cultures.
Latest News

Our laboratory comes together for a Christmas gift exchange party. It’s a wonderful tradition that brings us closer, as we share thoughtful gifts, laughter, and holiday cheer. The celebration is more than just exchanging presents—it’s a chance to strengthen our connections, create joyful memories, and embrace the true spirit of the season.

As the winter solstice approaches, the 2024 Fall semester is coming to an end. To celebrate this occasion, our lab held an intimate party to thank all the members for their hard work during the past semester. Here are some fun moments captured during the party:

Our lab members attend ACM Multimedia 2024 with the work “TimeNeRF: Building Generalizable Neural Radiance Fields across Time from Few-Shot Input Views”.

Professor and our lab members attend ICIP 2024 with three works: Lipface: Lipschitz-Conditioned for Resolution Robus Face Recognition Aerial view river landform video segmentation: A weakly supervised context-aware temporal consistency distillation approach Two Heads Better than One: Dual Degradation Representation for Blind Super-Resolution

Our lab members with the work “DetailSemNet: Elevating Signature Verification with Captured Details and Semantics by Feature Disentanglement and Re-entanglement”.