Methods > Computer Vision > Feature Extractors

NAS-FPN is a Feature Pyramid Network that is discovered via Neural Architecture Search in a novel scalable search space covering all cross-scale connections. The discovered architecture consists of a combination of top-down and bottom-up connections to fuse features across scales

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

Latest Papers

PAPER DATE
OPANAS: One-Shot Path Aggregation Network Architecture Search for Object Detection
| TingTing LiangYongtao WangZhi TangGuosheng HuHaibin Ling
2021-03-08
Simple Copy-Paste is a Strong Data Augmentation Method for Instance Segmentation
| Golnaz GhiasiYin CuiAravind SrinivasRui QianTsung-Yi LinEkin D. CubukQuoc V. LeBarret Zoph
2020-12-13
Rethinking Pre-training and Self-training
| Barret ZophGolnaz GhiasiTsung-Yi LinYin CuiHanxiao LiuEkin D. CubukQuoc V. Le
2020-06-11
SP-NAS: Serial-to-Parallel Backbone Search for Object Detection
Chenhan Jiang Hang Xu Wei Zhang Xiaodan Liang Zhenguo Li
2020-06-01
SpineNet: Learning Scale-Permuted Backbone for Recognition and Localization
| Xianzhi DuTsung-Yi LinPengchong JinGolnaz GhiasiMingxing TanYin CuiQuoc V. LeXiaodan Song
2019-12-10
MnasFPN: Learning Latency-aware Pyramid Architecture for Object Detection on Mobile Devices
| Bo ChenGolnaz GhiasiHanxiao LiuTsung-Yi LinDmitry KalenichenkoHartwig AdamsQuoc V. Le
2019-12-02
Learning Data Augmentation Strategies for Object Detection
| Barret ZophEkin D. CubukGolnaz GhiasiTsung-Yi LinJonathon ShlensQuoc V. Le
2019-06-26
NAS-FPN: Learning Scalable Feature Pyramid Architecture for Object Detection
| Golnaz GhiasiTsung-Yi LinRuoming PangQuoc V. Le
2019-04-16
ShapeMask: Learning to Segment Novel Objects by Refining Shape Priors
| Wei-cheng KuoAnelia AngelovaJitendra MalikTsung-Yi Lin
2019-04-05

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