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• W2936599103 abstract "Current state-of-the-art convolutional architectures for object detection are manually designed. Here we aim to learn a better architecture of feature pyramid network for object detection. We adopt Neural Architecture Search and discover a new feature pyramid architecture in a novel scalable search space covering all cross-scale connections. The discovered architecture, named NAS-FPN, consists of a combination of top-down and bottom-up connections to fuse features across scales. NAS-FPN, combined with various backbone models in the RetinaNet framework, achieves better accuracy and latency tradeoff compared to state-of-the-art object detection models. NAS-FPN improves mobile detection accuracy by 2 AP compared to state-of-the-art SSDLite with MobileNetV2 model in [32] and achieves 48.3 AP which surpasses Mask R-CNN [10] detection accuracy with less computation time." @default.
• W2936599103 created "2019-04-25" @default.
• W2936599103 creator A5002729731 @default.
• W2936599103 creator A5029917821 @default.
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• W2936599103 date "2019-04-16" @default.
• W2936599103 modified "2023-09-23" @default.
• W2936599103 title "NAS-FPN: Learning Scalable Feature Pyramid Architecture for Object Detection" @default.
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• W2936599103 hasPublicationYear "2019" @default.
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