Yuanhong Chen

Research Interests

Computer Vision Deep learning

Mr Yuanhong Chen

Research Fellow

School of Computer Science and Information Technology

College of Engineering and Information Technology

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 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.

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

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 Scopus12 WoS10 Europe PMC3
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 Scopus12 WoS11 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 Scopus34 WoS27 Europe PMC13
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 Scopus30 WoS25 Europe PMC21
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 Scopus34 WoS26 Europe PMC8

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 Mm 2025 Proceedings of the 33rd ACM International Conference on Multimedia Co Located with mm 2025 (pp. 7510-7518). IRELAND, Dublin: ASSOC COMPUTING MACHINERY.
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 Scopus1
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 Scopus15
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 Scopus12 WoS6
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 Scopus8 WoS13
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 Scopus163 WoS124
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 Scopus115 WoS93
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 Scopus28 WoS30
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 Scopus17 WoS28
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 WoS11
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 Scopus17 WoS23
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 Scopus114 WoS94
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 Scopus67 WoS58
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 Scopus17 WoS15
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 Scopus443 WoS334
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 Scopus59 WoS58

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..

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