By Professor Maurice Pagnucco

Preprints

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://dx.doi.org/10.48550/arxiv.2310.12386

Li P; Yang Y; Pagnucco M; Song Y, 2022, Explainability in Graph Neural Networks: An Experimental Survey, http://dx.doi.org/10.48550/arxiv.2203.09258

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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