By Associate Professor Piotr Koniusz

Conference Papers

Shao W; Prabowo A; Zhao S; Tan S; Koniusz P; Chan J; Hei X; Feest B; Salim FD, 2019, 'Flight delay prediction using airport situational awareness map', in GIS Proceedings of the ACM International Symposium on Advances in Geographic Information Systems, pp. 432 - 435, http://dx.doi.org/10.1145/3347146.3359079

Wang L; Koniusz P; Huynh D, 2019, 'Hallucinating IDT descriptors and I3D optical flow features for action recognition with CNNs', in Proceedings of the IEEE International Conference on Computer Vision, pp. 8697 - 8707, http://dx.doi.org/10.1109/ICCV.2019.00879

Zhang H; Dai Y; Li H; Koniusz P, 2019, 'Deep stacked hierarchical multi-patch network for image deblurring', in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 5971 - 5979, http://dx.doi.org/10.1109/CVPR.2019.00613

Zhang H; Zhang J; Koniusz P, 2019, 'Few-shot learning via saliency-guided hallucination of samples', in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 2765 - 2774, http://dx.doi.org/10.1109/CVPR.2019.00288

Zhang H; Koniusz P, 2019, 'Power normalizing second-order similarity network for few-shot learning', in Proceedings 2019 IEEE Winter Conference on Applications of Computer Vision Wacv 2019, pp. 1185 - 1193, http://dx.doi.org/10.1109/WACV.2019.00131

Shiri F; Yu X; Porikli F; Hartley R; Koniusz P, 2019, 'Recovering faces from portraits with auxiliary facial attributes', in Proceedings 2019 IEEE Winter Conference on Applications of Computer Vision Wacv 2019, pp. 406 - 415, http://dx.doi.org/10.1109/WACV.2019.00049

Sun K; Koniusz P; Wang Z, 2019, 'Fisher-Bures adversary graph convolutional networks', in 35th Conference on Uncertainty in Artificial Intelligence Uai 2019

Sun K; Koniusz P; Wang Z, 2019, 'Fisher-Bures Adversary Graph Convolutional Networks', in Proceedings of Machine Learning Research, pp. 465 - 475

Zhang H; Koniusz P, 2019, 'Model selection for generalized zero-shot learning', in Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, pp. 198 - 204, http://dx.doi.org/10.1007/978-3-030-11012-3_16

Koniusz P; Zhang H; Porikli F, 2018, 'A Deeper Look at Power Normalizations', in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 5774 - 5783, http://dx.doi.org/10.1109/CVPR.2018.00605

Zhang H; Koniusz P, 2018, 'Zero-Shot Kernel Learning', in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 7670 - 7679, http://dx.doi.org/10.1109/CVPR.2018.00800

Shiri F; Yu X; Porikli F; Hartley R; Koniusz P, 2018, 'Identity-preserving face recovery from portraits', in Proceedings 2018 IEEE Winter Conference on Applications of Computer Vision Wacv 2018, pp. 102 - 111, http://dx.doi.org/10.1109/WACV.2018.00018

Tas Y; Koniusz P, 2018, 'CNN-based action recognition and supervised domain adaptation on 3D body skeletons via kernel feature maps', in British Machine Vision Conference 2018 Bmvc 2018

Koniusz P; Tas Y; Zhang H; Harandi M; Porikli F; Zhang R, 2018, 'Museum Exhibit Identification Challenge for the Supervised Domain Adaptation and Beyond', in Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, pp. 815 - 833, http://dx.doi.org/10.1007/978-3-030-01270-0_48

Lin TY; Maji S; Koniusz P, 2018, 'Second-Order Democratic Aggregation', in Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, pp. 639 - 656, http://dx.doi.org/10.1007/978-3-030-01219-9_38

Shiri F; Yu X; Koniusz P; Porikli F, 2017, 'Face Destylization', in Dicta 2017 2017 International Conference on Digital Image Computing Techniques and Applications, pp. 1 - 8, http://dx.doi.org/10.1109/DICTA.2017.8227432

Shiri F; Yu X; Koniusz P; Porikli F, 2017, 'Face Destylization', in Guo Y; Li H; Cai W; Murshed M; Wang Z; Gao J; Feng DD (eds.), 2017 INTERNATIONAL CONFERENCE ON DIGITAL IMAGE COMPUTING - TECHNIQUES AND APPLICATIONS (DICTA), IEEE, AUSTRALIA, Sydney, pp. 427 - 434, presented at International Conference on Digital Image Computing - Techniques and Applications (DICTA), AUSTRALIA, Sydney, 29 November 2017 - 01 December 2017

Koniusz P; Tas Y; Porikli F, 2017, 'Domain adaptation by mixture of alignments of second- or higher-order scatter tensors', in Proceedings 30th IEEE Conference on Computer Vision and Pattern Recognition Cvpr 2017, pp. 7139 - 7148, http://dx.doi.org/10.1109/CVPR.2017.755

Cherian A; Koniusz P; Gould S, 2017, 'Higher-order pooling of cnn features via kernel linearization for action recognition', in Proceedings 2017 IEEE Winter Conference on Applications of Computer Vision Wacv 2017, pp. 130 - 138, http://dx.doi.org/10.1109/WACV.2017.22

Koniusz P; Cherian A, 2016, 'Sparse coding for third-order super-symmetric tensor descriptors with application to texture recognition', in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 5395 - 5403, http://dx.doi.org/10.1109/CVPR.2016.582

Koniusz P; Cherian A; Porikli F, 2016, 'Tensor representations via kernel linearization for action recognition from 3D skeletons', in Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, pp. 37 - 53, http://dx.doi.org/10.1007/978-3-319-46493-0_3

Mairal J; Koniusz P; Harchaoui Z; Schmid C, 2014, 'Convolutional kernel networks', in Advances in Neural Information Processing Systems, pp. 2627 - 2635

Koniusz P; Mikolajczyk K, 2011, 'Soft assignment of visual words as linear coordinate coding and optimisation of its reconstruction error', in Proceedings International Conference on Image Processing Icip, pp. 2413 - 2416, http://dx.doi.org/10.1109/ICIP.2011.6116129

Koniusz P; Mikolajczyk K, 2011, 'Spatial coordinate coding to reduce histogram representations, dominant angle and colour pyramid match', in Proceedings International Conference on Image Processing Icip, pp. 661 - 664, http://dx.doi.org/10.1109/ICIP.2011.6116639

Koniusz P; Mikolajczyk K, 2010, 'On a quest for image descriptors based on unsupervised segmentation maps', in Proceedings International Conference on Pattern Recognition, pp. 762 - 765, http://dx.doi.org/10.1109/ICPR.2010.192

Koniusz P; Mikolajczyk K, 2009, 'Segmentation based interest points and evaluation of unsupervised image segmentation methods', in British Machine Vision Conference Bmvc 2009 Proceedings, http://dx.doi.org/10.5244/C.23.24

Conference Abstracts

Wang L; Koniusz P, (eds.), 2026, 'Feature Hallucination for Self-supervised Action Recognition (Abstract Reprint)', in FORTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE, AAAI-26, VOL 40 NO 47, ASSOC ADVANCEMENT ARTIFICIAL INTELLIGENCE, SINGAPORE, pp. 39902 - 39902, presented at 40th AAAI Conference on Artificial Intelligence, SINGAPORE, 20 January 2026 - 27 January 2026

Preprints

Li J; Jiang P; Saleem H; Wang Z; Koniusz P; Salim FD, 2026, SGNO: Spectral Generator Neural Operators for Stable Long Horizon PDE Rollouts, http://dx.doi.org/10.48550/arxiv.2602.18801

Dong J; Zhang Y; Zhu H; Ong Y-S; Koniusz P, 2026, Hierarchically Robust Zero-shot Vision-language Models, https://arxiv.org/abs/2604.18867v1

Bo Y; Zhu Y; Koniusz P; Zhang H, 2026, Focus on Background: Exploring SAM's Potential in Few-shot Medical Image Segmentation with Background-centric Prompting, https://arxiv.org/abs/2603.21287v1

Wijesinghe A; Zhu H; Koniusz P, 2025, Graph Self-Supervised Learning with Learnable Structural and Positional Encodings, https://doi.org/10.1145/3696410.3714745

Ni Y; Koniusz P, 2024, CHAIN: Enhancing Generalization in Data-Efficient GANs via lipsCHitz continuity constrAIned Normalization, https://arxiv.org/abs/2404.00521v6

Haghighat M; Moghadam P; Mohamed S; Koniusz P, 2023, Pre-training with Random Orthogonal Projection Image Modeling, https://arxiv.org/abs/2310.18737v2

Zhang Y; Zhu H; Yang M; Liu J; Ying R; King I; Koniusz P, 2023, Understanding and Mitigating Hyperbolic Dimensional Collapse in Graph Contrastive Learning, https://doi.org/10.1145/3690624.3709249

Prabowo A; Xue H; Shao W; Koniusz P; Salim FD, 2023, Traffic Forecasting on New Roads Using Spatial Contrastive Pre-Training (SCPT), http://dx.doi.org/10.48550/arxiv.2305.05237

Li Z; Koniusz P; Zhang L; Pagendam DE; Moghadam P, 2023, Exploiting Field Dependencies for Learning on Categorical Data, https://arxiv.org/abs/2307.09321v1

Prabowo A; Xue H; Shao W; Koniusz P; Salim FD, 2023, Message Passing Neural Networks for Traffic Forecasting, http://dx.doi.org/10.48550/arxiv.2305.05740

Rahman S; Koniusz P; Wang L; Zhou L; Moghadam P; Sun C, 2023, Learning Partial Correlation based Deep Visual Representation for Image Classification, http://dx.doi.org/10.48550/arxiv.2304.11597

Zhu H; Koniusz P, 2023, Transductive Few-shot Learning with Prototype-based Label Propagation by Iterative Graph Refinement, https://arxiv.org/abs/2304.11598v1

Lu C; Zhu H; Koniusz P, 2023, From Saliency to DINO: Saliency-guided Vision Transformer for Few-shot Keypoint Detection, https://arxiv.org/abs/2304.03140v1

Wang L; Koniusz P, 2023, 3Mformer: Multi-order Multi-mode Transformer for Skeletal Action Recognition, https://arxiv.org/abs/2303.14474v1

Prabowo A; Shao W; Xue H; Koniusz P; Salim FD, 2023, Because Every Sensor Is Unique, so Is Every Pair: Handling Dynamicity in Traffic Forecasting, http://dx.doi.org/10.48550/arxiv.2302.09956

Zhang Y; Zhu H; Song Z; Koniusz P; King I, 2022, Spectral Feature Augmentation for Graph Contrastive Learning and Beyond, https://arxiv.org/abs/2212.01026v1

Zhang S; Murray N; Wang L; Koniusz P, 2022, Time-rEversed diffusioN tEnsor Transformer: A new TENET of Few-Shot Object Detection, https://arxiv.org/abs/2210.16897v1

Wang L; Koniusz P, 2022, Uncertainty-DTW for Time Series and Sequences, https://arxiv.org/abs/2211.00005v1

Wang L; Koniusz P, 2022, Temporal-Viewpoint Transportation Plan for Skeletal Few-shot Action Recognition, https://arxiv.org/abs/2210.16820v1

Zhang Y; Zhu H; Song Z; Koniusz P; King I, 2022, COSTA: Covariance-Preserving Feature Augmentation for Graph Contrastive Learning, https://doi.org/10.1145/3534678.3539425

Zhang Y; Zhu H; Meng Z; Koniusz P; King I, 2022, Graph-adaptive Rectified Linear Unit for Graph Neural Networks, https://doi.org/10.1145/3485447.3512159

Zhang H; Li H; Koniusz P, 2022, Multi-level Second-order Few-shot Learning, https://doi.org/10.1109/TMM.2022.3142955

Zhu H; Sun K; Koniusz P, 2022, Contrastive Laplacian Eigenmaps, https://arxiv.org/abs/2201.05493v1

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