Departmental Seminar: “Trustworthy AI for Subtle Visual Signals” – Dr Xinqi Fan, Manchester Metropolitan University
| Dates: | 7 October 2026 |
|---|
| Times: | 13:15 - 14:15 |
|---|
| What is it: | Seminar |
|---|
| Organiser: | Department of Computer Science |
|---|
| How much: | Free |
|---|
| Who is it for: | University staff, Current University students |
|---|
| Speaker: | Dr Xinqi Fan |
|---|
|
Abstract
Some of the informative visual evidence appears as small changes that are easy to overlook. Learning from these subtle signals remains challenging for AI systems. This talk presents our research on trustworthy AI for recognising subtle visual signals across facial and medical image understanding. First, facial micro-expressions are brief and subtle movements that may provide cues to underlying affective states. We investigate robust representation learning through self-supervised motion learning to capture their fine-grained dynamics. We further demonstrate that the learned representations can support both micro-expression analysis and video generation. Second, we turn to medical imaging for ulcerative colitis, where small differences in mucosal appearance can affect disease assessment. To ground predictions in clinical knowledge, we develop an endoscopic-informed spiral-scanning strategy for state-space models that captures meaningful visual patterns. We also introduce a mixture of low-rank vision-language experts to incorporate clinical concepts into model training and reasoning. Finally, small differences between training and deployment conditions can reduce model performance. We address this distribution shift through retrieval-augmented test-time adaptation and reflective multi-agent reasoning, enabling AI systems to use new evidence and self-improve their predictions without conventional retraining.
Speaker
Dr Xinqi Fan
Role: Lecturer in AI in the School of Computing and Mathematics
Organisation: Manchester Metropolitan University
Biography: Dr Xinqi Fan is a Lecturer in AI in the School of Computing and Mathematics at Manchester Metropolitan University. He received his PhD from City University of Hong Kong and his MEng from the University of Western Australia. He has also conducted research at King Abdullah University of Science and Technology and the Chinese University of Hong Kong. His research interests include deep learning, computer vision, and multimodal learning, with applications in affective computing and medical image analysis. His work has appeared in venues including CVPR, ICCV, ACM MM, MICCAI, IEEE TAFFC, IEEE TIP, IEEE JBHI, and TMLR. He has organised challenges at ACM MM 2025 and FG 2026, as well as a workshop at ICME 2025. His team won first place in the ISBI 2026 multimodal ulcerative colitis grading challenge.
Travel and Contact Information
Find event
Kilburn_TH 1.3
Kilburn Building
Manchester
