Welcome

I am looking for motivated PhD students to work on scene understanding, 3D modeling, generative AI, etc. 

Scholarships are available [link]. Feel free to drop me an email if you are interested.

Bio

I am now working as a research scientist at Institute for Infocomm Research (I2R), Agency for Science, Technology & Research (A*STAR), Singapore. I obtained my PhD degree from School of Computer Science, University of Adelaide, in Dec. 2015, under the supervision of 

Prof. Chunhua Shen.

Award & Recognition

SG100 Women in Tech honoree, 2023.

MTC Young Individual Research Grant Awardee, 2022.

World’s Top 2% scientists by Stanford University, 2021, 2022, 2023.

Dean's Commendation for Doctoral Thesis Excellence, 2015.

Research Interests

Machine Learning: Representation learning, Few-shot learning, Unsupervised learning, Deep generative models, etc.

Applications: Computer vision, Robotic vision, Data analysis, etc.

Academic Services

Associate editor of IEEE Transaction on Multimedia (Oct 2024 - Sept 2026).

Associate editor of IEEE Transactions on Circuits and Systems for Video Technology (Jan 2023 - Dec 2024). 

Area Chair and Tutorial & Demo Chair for 3DV 2025.

PhD Supervision

• Kennard Yanting Chan (ACIS)

• Chaoyue Song (SINGA)

• Cheng Chen (SINGA)

• Ryotaro Kakuda (SINGA)

• Jinnapat Yana (SINGA,  Co-Supervise)

Selected Publications

For a full publication list, please refer to my Google Scholar page: [link]

• F. Liu, C. Shen, G. Lin, I. Reid. Learning Depth from Single Monocular Images Using Deep Convolutional Neural Fields. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2015. [pdf]. [code].

• F. Liu, C. Shen, G. Lin. Deep Convolutional Neural Fields for Depth Estimation from a Single Images. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2015. [pdf].

• F. Liu, G. Lin, C. Shen. CRF Learning with CNN Features for Image Segmentation. Pattern Recognition (PR), 2015. [pdf].

• F. Liu, L. Zhou, C. Shen, J. Yin. Multiple Kernel Learning in the Primal for Multimodal Alzheimer's Disease Classification. IEEE Journal of Biomedical and Health Informatics, 2014. [pdf].

• F. Liu, C. Shen, I. Reid, A. Hengel. Online Unsupervised Feature Learning for Visual Tracking. Image and Vision Computing (IVC), 2016. [pdf].

• G. Lin, F. Liu, C. Shen, J. Wu, H. Shen. Structured Learning of Binary Codes with Column Generation. International Journal of Computer Vision (IJCV), 2016. [pdf]. 

• C. Shen, J. Kim, F. Liu, L. Wang, A. Hengel.  Efficient Dual Approach to Distance Metric Learning. IEEE Transactions on Neural Network and Learning System (TNNLS), 2014. [pdf].

• F. Liu, R. Qiao, C. Shen, L. Luo. Designing ensemble learning algorithms using kernel methods. International Journal of Machine Intelligence and Sensory Signal Processing, 2017. [pdf].

• F. Liu, G. Lin, C. Shen. Discriminative Training of Deep Fully-connected Continuous CRF with Task-specific Loss.  IEEE Transactions on Image Processing (TIP), 2017. [pdf]. 

• F. Liu, G. Lin, R. Qiao, C. Shen. Structured learning of tree potentials in CRF for image segmentation. IEEE Transactions on Neural Network and Learning System (TNNLS), 2018. [pdf]

• G. Lin, F. Liu*, A. Milan, C. Shen, I. Reid. RefineNet: Multi-Path Refinement Networks for Dense Prediction. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2019.  (* indicates corresponding author)

• C. Zhang, G. Lin, F. Liu, R. Yao, C. Shen. CANet: Class-Agnostic Segmentation Networks with Iterative Refinement and Attentive Few-Shot Learning. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2019. [pdf]

• Z. Liu, G. Lin, S. Yang, F. Liu, W. Lin, W. Goh. Towards Robust Curve Text Detection with Conditional Spatial Expansion. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2019. [pdf]

• C. Zhang, G. Lin, F. Liu, J. Guo, Q. Wu, R. Yao. Pyramid Graph Networks with Connection Attentions for Region-Based One-Shot Semantic Segmentation. International Conference on Computer Vision (ICCV), 2019. [pdf]

• H. Luo, G. Lin, Zichuan Liu, F. Liu, Z. Tang, Y. Yao. SegEQA: Video Segmentation Based Visual Attention for Embodied Question Answering. International Conference on Computer Vision (ICCV), 2019. [pdf]

• F. He, F. Liu*, R. Yao, G. Lin. Local Fusion Networks with Chained Residual Pooling for Video Action Recognition. Image and Vision Computing (IVC), 2019. (* indicates corresponding author)

• W. Liu, C. Zhang, G. Lin, F. Liu. CRNet: Cross-Reference Networks for Few-Shot Segmentation. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2020.

• R. Li, G. Lin, T. He, F. Liu, C. Shen. Hcrf-flow: Scene Flow from Point Clouds with Continuous High-order CRFs and Position-Aware Flow Embedding. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2021. 

• C. Song, J. Wei, R. Li, F. Liu, G. Lin. 3D Pose Transfer With Correspondence Learning And Mesh Refinement. Advances in Neural Information Processing Systems (NeurIPS) 2021. [code].

• W. Zhang, X. Xu, F. Liu, L. Zhang, CS. Foo. On Automatic Data Augmentation for 3D Point Cloud Classification. British Machine Vision Conference (BMVC) 2021. [code].

• H. Luo, G. Lin, Y. Yao, F. Liu, Z. Liu, Z. Tang.  Depth and Video Segmentation Based Visual Attention for Embodied Question Answering. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2022. 

• H. Shi, J. Wei, R. Li, F. Liu, G. Lin. Weakly supervised segmentation on outdoor 4d point clouds with temporal matching and spatial graph propagation. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2022. 

• F. Liu, G. Lin, CK. Joshi, CS. Foo, J. Lin. Point Discriminative Learning for Unsupervised Representation Learning on 3D Point Clouds. International Conference on 3D Vision (3DV) 2022.

• CK. Joshi, F. Liu, X. Xun, J. Lin, CS. Foo. On representation knowledge distillation for graph neural networks. IEEE Transactions on Neural Network and Learning System (TNNLS), 2022. [code].