Better object detection with few examples by using image rather than proposals with Meta-DETR
Better object detection with few examples by using image rather than proposals with Meta-DETR
Meta-DETR: Image-Level Few-Shot Detection with Inter-Class Correlation Exploitation
arXiv paper abstract https://arxiv.org/abs/2208.00219v1
arXiv PDF paper https://arxiv.org/pdf/2208.00219v1.pdf
Few-shot object detection has been extensively investigated by incorporating meta-learning into region-based detection frameworks.
... paradigm ... still constrained by ... (i) low-quality region proposals for novel classes and (ii) negligence of the inter-class correlation among different classes.
... design Meta-DETR, which (i) is the first image-level few-shot detector, and (ii) introduces a novel inter-class correlational meta-learning strategy to capture and leverage the correlation among different classes
... Meta-DETR works entirely at image level without any region proposals, which circumvents the constraint of inaccurate proposals
... Meta-DETR ... simultaneously attend to multiple support classes within a single feedforward, which allows to capture the inter-class correlation among different classes, thus significantly reducing the misclassification ... and enhancing ... generalization to novel classes.
... show that the proposed Meta-DETR outperforms state-of-the-art methods by large margins ...
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