By Professor Maurice Pagnucco

Preprints

Tang Q; Fan L; Pagnucco M; Song Y, 2025, Prototype-Based Image Prompting for Weakly Supervised Histopathological Image Segmentation, http://dx.doi.org/10.48550/arxiv.2503.12068

Fu Y; Tompkins A; Song Y; Pagnucco M, 2025, Structure based SAT dataset for analysing GNN generalisation, http://dx.doi.org/10.48550/arxiv.2502.11410

Fan L; Fan D; Hu Z; Ding Y; Di D; Yi K; Pagnucco M; Song Y, 2024, MANTA: A Large-Scale Multi-View and Visual-Text Anomaly Detection Dataset for Tiny Objects, http://dx.doi.org/10.48550/arxiv.2412.04867

Cong C; Xuan S; Liu S; Pagnucco M; Zhang S; Song Y, 2024, Dataset Distillation for Histopathology Image Classification, http://dx.doi.org/10.48550/arxiv.2408.09709

Fan L; Ding Y; Fan D; Wu Y; Chu H; Pagnucco M; Song Y, 2023, An annotated grain kernel image database for visual quality inspection, http://dx.doi.org/10.48550/arxiv.2401.08599

Fan L; Ding Y; Fan D; Wu Y; Pagnucco M; Song Y, 2023, Identifying the Defective: Detecting Damaged Grains for Cereal Appearance Inspection, http://dx.doi.org/10.48550/arxiv.2311.11901

Hengst B; Pagnucco M; Rajaratnam D; Sammut C; Thielscher M, 2023, Online Learning and Planning in Cognitive Hierarchies, http://arxiv.org/abs/2310.12386v1

Huang R; Xue H; Pagnucco M; Salim F; Song Y, 2023, Vision-based Multi-future Trajectory Prediction: A Survey, http://dx.doi.org/10.48550/arxiv.2302.10463

Fan L; Ding Y; Fan D; Di D; Pagnucco M; Song Y, 2022, GrainSpace: A Large-scale Dataset for Fine-grained and Domain-adaptive Recognition of Cereal Grains, http://dx.doi.org/10.48550/arxiv.2203.05306

Bai Y; Wang J; Long Y; Hu B; Song Y; Pagnucco M; Guan Y, 2021, Discriminative Latent Semantic Graph for Video Captioning, http://dx.doi.org/10.48550/arxiv.2108.03662

Hengst B; Pagnucco M; Rajaratnam D; Sammut C; Thielscher M, 2018, Perceptual Context in Cognitive Hierarchies, http://dx.doi.org/10.48550/arxiv.1801.02270

Nakata NM; Hamacher DW; Warren J; Byrne A; Pagnucco M; Harley R; Venugopal S; Thorpe K; Neville R; Bolt R, 2014, Using Modern Technologies to Capture and Share Indigenous Astronomical Knowledge, http://dx.doi.org/10.48550/arxiv.1409.1307

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