By Associate Professor Piotr Koniusz

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

Wang L; Liu J; Koniusz P, 2021, 3D Skeleton-based Few-shot Action Recognition with JEANIE is not so Naïve, https://arxiv.org/abs/2112.12668v1

Ni Y; Koniusz P; Hartley R; Nock R, 2021, Manifold Learning Benefits GANs, https://arxiv.org/abs/2112.12618v2

Lu C; Koniusz P, 2021, Few-shot Keypoint Detection with Uncertainty Learning for Unseen Species, https://arxiv.org/abs/2112.06183v3

Simon C; Koniusz P; Harandi M, 2021, Meta-Learning for Multi-Label Few-Shot Classification, https://arxiv.org/abs/2110.13494v1

Tas Y; Koniusz P, 2021, Simple Dialogue System with AUDITED, https://arxiv.org/abs/2110.11881v1

Simon C; Koniusz P; Petersson L; Han Y; Harandi M, 2021, Towards a Robust Differentiable Architecture Search under Label Noise, https://arxiv.org/abs/2110.12197v1

Wang L; Sun K; Koniusz P, 2021, High-order Tensor Pooling with Attention for Action Recognition, https://arxiv.org/abs/2110.05216v4

Zhu H; Koniusz P, 2021, REFINE: Random RangE FInder for Network Embedding, https://doi.org/10.1145/3459637.3482168

Zhu H; Koniusz P, 2021, Graph Convolutional Network with Generalized Factorized Bilinear Aggregation, https://arxiv.org/abs/2107.11666v1

Shao W; Prabowo A; Zhao S; Koniusz P; Salim FD, 2021, Predicting Flight Delay with Spatio-Temporal Trajectory Convolutional Network and Airport Situational Awareness Map, http://dx.doi.org/10.48550/arxiv.2105.08969

Koniusz P; Wang L; Cherian A, 2020, Tensor Representations for Action Recognition, https://doi.org/10.1109/TPAMI.2021.3107160

Koniusz P; Zhang H, 2020, Power Normalizations in Fine-grained Image, Few-shot Image and Graph Classification, https://doi.org/10.1109/TPAMI.2021.3107164

Hou W; Suominen H; Koniusz P; Caldwell S; Gedeon T, 2020, A Token-wise CNN-based Method for Sentence Compression, http://dx.doi.org/10.48550/arxiv.2009.11260

Wang X; Salim FD; Ren Y; Koniusz P, 2020, Relation Embedding for Personalised POI Recommendation, http://dx.doi.org/10.48550/arxiv.2002.03461

Yu X; Zhuang Z; Koniusz P; Li H, 2020, 6DoF Object Pose Estimation via Differentiable Proxy Voting Loss, https://arxiv.org/abs/2002.03923v2

Wang L; Koniusz P, 2020, Self-supervising Action Recognition by Statistical Moment and Subspace Descriptors, https://doi.org/10.1145/3474085.3475572

Zhang H; Zhang L; Qi X; Li H; Torr PHS; Koniusz P, 2020, Few-shot Action Recognition with Permutation-invariant Attention, https://arxiv.org/abs/2001.03905v3

Zhang H; Koniusz P; Jian S; Li H; Torr PHS, 2020, Rethinking Class Relations: Absolute-relative Supervised and Unsupervised Few-shot Learning, https://arxiv.org/abs/2001.03919v4

Zhang H; Torr PHS; Koniusz P, 2020, Improving Few-shot Learning by Spatially-aware Matching and CrossTransformer, https://arxiv.org/abs/2001.01600v2

Prabowo A; Koniusz P; Shao W; Salim FD, 2019, COLTRANE: ConvolutiOnaL TRAjectory NEtwork for Deep Map Inference, http://dx.doi.org/10.48550/arxiv.1909.11048

Wang L; Huynh DQ; Koniusz P, 2019, A Comparative Review of Recent Kinect-based Action Recognition Algorithms, https://doi.org/10.1109/TIP.2019.2925285

Wang L; Koniusz P; Huynh DQ, 2019, Hallucinating IDT Descriptors and I3D Optical Flow Features for Action Recognition with CNNs, https://arxiv.org/abs/1906.05910v2

Koniusz P; Zhang H; Porikli F, 2018, A Deeper Look at Power Normalizations, https://arxiv.org/abs/1806.09183v1

Zhang R; Tas Y; Koniusz P, 2018, Artwork Identification from Wearable Camera Images for Enhancing Experience of Museum Audiences, https://arxiv.org/abs/1806.09084v1

Tas Y; Koniusz P, 2018, CNN-based Action Recognition and Supervised Domain Adaptation on 3D Body Skeletons via Kernel Feature Maps, https://arxiv.org/abs/1806.09078v1

Shiri F; Yu X; Porikli F; Koniusz P, 2018, Face Destylization, https://arxiv.org/abs/1802.01237v1

Zhang H; Koniusz P, 2018, Zero-Shot Kernel Learning, https://arxiv.org/abs/1802.01279v2

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