Dr Yuanhong Chen

Postdoc Researcher

Australian Institute for Machine Learning

Division of Research and Innovation

Dr. Yuanhong Chen is a postdoctoral researcher at the Australian Institute for Machine Learning, specialising in computer vision, multimodal learning, and generative models. He holds a PhD in Computer Science and works on bridging medical image analysis and audio-visual perception with deep learning. His current research explores integrating large language models for cross-modal understanding and reasoning.

My Research
Career
Publications
Contact

My previous research focused on multimodal learning, audio-visual perception, and spatial audio generation. My current research explores the integration of large language models for cross-modal understanding and reasoning, with applications in audio-visual learning and generative modelling.

Education

Date Institution name Country Title

2021 - 2024 University of Adelaide Australia PhD
Research Interests

Computer Vision Deep learning

Date	Institution name	Country	Title
2021 - 2024	University of Adelaide	Australia	PhD

Journals

Year	Citation
2025	Wang, C., Liu, F., Chen, Y., Frazer, H., & Carneiro, G. (2025). Cross- and Intra-image Prototypical Learning for Multi-label Disease Diagnosis and Interpretation. IEEE Transactions on Medical Imaging, 44(6), 2568-2580. DOI Scopus7 WoS4 Europe PMC2
2024	Chen, Y., Liu, Y., Wang, C., Elliott, M., Kwok, C. F., Peña-Solorzano, C., . . . Carneiro, G. (2024). BRAIxDet: Learning to detect malignant breast lesion with incomplete annotations. Medical Image Analysis, 96, 103192-1-103192-13. DOI Scopus11 WoS9 Europe PMC6
2024	Wang, C., Chen, Y., Liu, F., Elliott, M., Kwok, C. F., Pena-Solorzano, C., . . . Carneiro, G. (2024). An Interpretable and Accurate Deep-learning Diagnosis Framework Modelled with Fully and Semi-supervised Reciprocal Learning. IEEE Transactions on Medical Imaging, 43(1), 392-404. DOI Scopus28 WoS20 Europe PMC11
2023	Frazer, H. M. L., Tang, J. S. N., Elliott, M. S., Kunicki, K. M., Hill, B., Karthik, R., . . . McCarthy, D. J. (2023). ADMANI: Annotated Digital Mammograms and Associated Non-Image Datasets. Radiology: Artificial Intelligence, 5(2), 1-7. DOI Scopus26 WoS23 Europe PMC19
2023	Tian, Y., Liu, F., Pang, G., Chen, Y., Liu, Y., Verjans, J. W., . . . Carneiro, G. (2023). Self-supervised pseudo multi-class pre-training for unsupervised anomaly detection and segmentation in medical images. Medical Image Analysis, 90, 102930-1-102930-11. DOI Scopus29 WoS18 Europe PMC6

Conference Papers

Year	Citation
2025	Liu, Y., Chen, Y., Wang, H., Belagiannis, V., Reid, I., & Carneiro, G. (2025). ItTakesTwo: Leveraging Peer Representations for Semi-supervised LiDAR Semantic Segmentation. In Proceedings of the 18th European Conference on Computer Vision (ECCV, 2024) Vol. 15059 (pp. 81-99). Milan, Italy: Springer Science and Business Media Deutschland GmbH. DOI
2025	Chen, Y., Shimada, K., Simon, C., Ikemiya, Y., Shibuya, T., & Mitsufuji, Y. (2025). CCStereo: Audio-Visual Contextual and Contrastive Learning for Binaural Audio Generation. In Proceedings of the 33rd ACM International Conference on Multimedia (pp. 7510-7518). ACM. DOI
2024	Chen, Y., Wang, C., Liu, Y., Wang, H., & Carneiro, G. (2024). CPM: Class-Conditional Prompting Machine for Audio-Visual Segmentation. In Lecture Notes in Computer Sciences Vol. 15068 LNCS (pp. 438-456). Milan, Italy: Springer Nature Switzerland. DOI
2024	Chen, Y., Liu, Y., Wang, H., Liu, F., Wang, C., Frazer, H., & Carneiro, G. (2024). Unraveling Instance Associations: A Closer Look for Audio-Visual Segmentation. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2024) (pp. 26487-26497). Seattle, WA, USA: Institute of Electrical and Electronics Engineers (IEEE). DOI Scopus8
2024	Chen, Y., Liu, F., Wang, H., Wang, C., Liu, Y., Tian, Y., & Carneiro, G. (2024). BoMD: Bag of Multi-label Descriptors for Noisy Chest X-ray Classification. In Proceedings of the IEEE International Conference on Computer Vision (ICCV, 2023) (pp. 21227-21238). Online: IEEE. DOI Scopus11 WoS4
2023	Tian, Y., Pang, G., Liu, Y., Wang, C., Chen, Y., Liu, F., . . . Carneiro, G. (2023). Unsupervised Anomaly Detection in Medical Images with a Memory-Augmented Multi-level Cross-Attentional Masked Autoencoder. In Proceedings of the 14th International Workshop, Machine Learning in Medical Imaging (MLMI 2023), as published in Lecture Notes in Computer Science Vol. 14349 (pp. 11-21). Cham, Switzerland: Springer Nature. DOI Scopus5 WoS8
2023	Wang, H., Chen, Y., Ma, C., Avery, J. C., Hull, M. L., & Carneiro, G. (2023). Multi-Modal Learning With Missing Modality via Shared-Specific Feature Modelling. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2023) Vol. 2023-June (pp. 15878-15887). Vancouver, Canada: IEEE. DOI Scopus111 WoS75
2022	Chen, Y., Tian, Y., Pang, G., & Carneiro, G. (2022). Deep One-Class Classification via Interpolated Gaussian Descriptor. In Proceedings of the 36th AAAI Conference on Artificial Intelligence, AAAI 2022 Vol. 36 (pp. 383-392). Online: Association for the Advancement of Artificial Intelligence. DOI Scopus99 WoS74
2022	Tian, Y., Pang, G., Liu, F., Liu, Y., Wang, C., Chen, Y., . . . Carneiro, G. (2022). Contrastive Transformer-Based Multiple Instance Learning for Weakly Supervised Polyp Frame Detection. In Proceedings of the 25th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2022), as published in Lecture Notes in Computer Science Vol. 13433 (pp. 88-98). Online: Springer Link. DOI Scopus23 WoS25
2022	Chen, Y., Wang, H., Wang, C., Tian, Y., Liu, F., Liu, Y., . . . Carneiro, G. (2022). Multi-view Local Co-occurrence and Global Consistency Learning Improve Mammogram Classification Generalisation. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 13433 LNCS (pp. 3-13). Online: Springer. DOI Scopus19 WoS21
2022	Liu, F., Chen, Y., Tian, Y., Liu, Y., Wang, C., Belagiannis, V., & Carneiro, G. (2022). NVUM: Non-volatile Unbiased Memory for Robust Medical Image Classification. In Proceedings of the 25th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2022), as published in Lecture Notes in Computer Science Vol. 13433 (pp. 544-553). Singapore: Springer. DOI Scopus12 WoS10
2022	Wang, C., Chen, Y., Liu, Y., Tian, Y., Liu, F., McCarthy, D. J., . . . Carneiro, G. (2022). Knowledge Distillation to Ensemble Global and Interpretable Prototype-Based Mammogram Classification Models. In Proceedings of the 25th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2022), as published in Lecture Notes in Computer Science Vol. 13433 (pp. 14-24). Singapore: Springer. DOI Scopus11 WoS18
2022	Liu, F., Tian, Y., Chen, Y., Liu, Y., Belagiannis, V., & Carneiro, G. (2022). ACPL: Anti-curriculum Pseudo-labelling for Semi-supervised Medical Image Classification. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2022) Vol. 2022-June (pp. 20665-20674). New Orleans, Louisiana: IEEE. DOI Scopus102 WoS78
2022	Tian, Y., Liu, Y., Pang, G., Liu, F., Chen, Y., & Carneiro, G. (2022). Pixel-Wise Energy-Biased Abstention Learning for Anomaly Segmentation on Complex Urban Driving Scenes. In Proceedings, Part XXXIX of the 17th European Conference on Computer Vision (ECCV 2022), as published in Lecture Notes in Computer Science Vol. 13699 LNCS (pp. 246-263). Cham, Switzerland: Springer. DOI Scopus54 WoS42
2022	Wang, H., Zhang, J., Chen, Y., Ma, C., Avery, J., Hull, L., & Carneiro, G. (2022). Uncertainty-Aware Multi-modal Learning via Cross-Modal Random Network Prediction. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 13697 LNCS (pp. 200-217). Online: Springer Nature Switzerland. DOI Scopus15 WoS13
2021	Tian, Y., Pang, G., Chen, Y., Singh, R., Verjans, J. W., & Carneiro, G. (2021). Weakly-supervised Video Anomaly Detection with Robust Temporal Feature Magnitude Learning. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV 2021) Vol. abs/2101.10030 (pp. 4955-4966). virtual online: IEEE. DOI Scopus379 WoS266
2021	Tian, Y., Pang, G., Liu, F., Chen, Y., Shin, S. -H., Verjans, J. W., . . . Carneiro, G. (2021). Constrained Contrastive Distribution Learning for Unsupervised Anomaly Detection and Localisation in Medical Images. In Proceedings of the 24th Medical Image Computing and Computer Assisted Intervention (MICCAI 2021), as publisghed in Lecture Notes in Computer Science Vol. 12905 (pp. 128-140). Cham, Switzerland: Springer. DOI Scopus54 WoS54

Preprint

Year	Citation
2025	Chen, Y., Shimada, K., Simon, C., Ikemiya, Y., Shibuya, T., & Mitsufuji, Y. (2025). CCStereo: Audio-Visual Contextual and Contrastive Learning for Binaural Audio Generation.
2024	Chen, Y., Wang, C., Liu, Y., Wang, H., & Carneiro, G. (2024). CPM: Class-conditional Prompting Machine for Audio-visual Segmentation.
2023	Chen, Y., Liu, Y., Wang, H., Liu, F., Wang, C., Frazer, H., & Carneiro, G. (2023). Unraveling Instance Associations: A Closer Look for Audio-Visual Segmentation.
2023	Chen, Y., Liu, Y., Wang, C., Elliott, M., Kwok, C. F., Pena-Solorzano, C., . . . Carneiro, G. (2023). BRAIxDet: Learning to Detect Malignant Breast Lesion with Incomplete Annotations.
2022	Chen, Y., Liu, F., Wang, H., Wang, C., Tian, Y., Liu, Y., & Carneiro, G. (2022). BoMD: Bag of Multi-label Descriptors for Noisy Chest X-ray Classification.
2022	Tian, Y., Pang, G., Liu, Y., Wang, C., Chen, Y., Liu, F., . . . Carneiro, G. (2022). Unsupervised Anomaly Detection in Medical Images with a Memory-augmented Multi-level Cross-attentional Masked Autoencoder..

Position: Postdoc Researcher
Phone: 83135643
Email: yuanhong.chen@adelaide.edu.au
Campus: Lot 14
Building: Australian Institute for Machine Learning Building, floor Ground Floor
Room: G.33
Org Unit: Faculty of Sciences, Engineering and Technology

Dr Yuanhong Chen

Education

Research Interests

Journals

Conference Papers

Preprint

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