Semantic Scholar Open Access 2019 1703 sitasi

NAS-FPN: Learning Scalable Feature Pyramid Architecture for Object Detection

Golnaz Ghiasi Tsung-Yi Lin Ruoming Pang Quoc V. Le

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Abstrak

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.

Topik & Kata Kunci

Computer Science

Penulis (4)

Golnaz Ghiasi

Tsung-Yi Lin

Ruoming Pang

Quoc V. Le

Format Sitasi

APA MLA BibTeX

Ghiasi, G., Lin, T., Pang, R., Le, Q.V. (2019). NAS-FPN: Learning Scalable Feature Pyramid Architecture for Object Detection. https://doi.org/10.1109/CVPR.2019.00720

Akses Cepat

Lihat di Sumber doi.org/10.1109/CVPR.2019.00720

Informasi Jurnal

Tahun Terbit: 2019
Bahasa: en
Total Sitasi: 1703×
Sumber Database: Semantic Scholar
DOI: 10.1109/CVPR.2019.00720
Akses: Open Access ✓