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- W4384153359 abstract "Abstract Few-shot learning raises problems like supervisory collapse due to class imbalance and pseudo-labels unreliability, which seriously hinder the performance improvement of few-shot object detection. To solve the above problems, the paper proposed a model of few-shot object detection based on graph interaction multivariate prediction fusion. Firstly, the feature extraction network with a semantic attention mechanism is designed to adaptively adjust the new class features so that to obtain high-quality new classes pseudo-labels. Second, visual features are converted into graphical features by constructing graph interaction mechanisms using implicit relationships between objects to optimize and improve the accuracy of pseudo-labels. Finally, the multi-classifier object detection mechanism is used to achieve accurate classification and localization of few-shot objects. The simulation was designed to validate the performance of the proposed model in the MS-COCO dataset and PASCAL VOC dataset and compare it with existing state-of-the-art and representative models. The experiments prove that the detection results of the proposed model in this paper outperform other models in terms of visual effects and objective data." @default.
- W4384153359 created "2023-07-14" @default.
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- W4384153359 date "2023-07-14" @default.
- W4384153359 modified "2023-09-30" @default.
- W4384153359 title "GIMPF: Graph Interactive Multivariate Prediction Fusion for Few-shot Learning of Object Detectors" @default.
- W4384153359 doi "https://doi.org/10.21203/rs.3.rs-3159617/v1" @default.
- W4384153359 hasPublicationYear "2023" @default.
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