AugFPN: Improving Multi-Scale Feature Learning for Object Detection

By Chaoxu Guo, Bin Fan, Qian Zhang, Shiming Xiang, Chunhong Pan. Computer Vision and Pattern Recognition, 2019

495 citations recorded by the source corpus.

Current state-of-the-art detectors typically exploit feature pyramid to detect objects at different scales. Among them, FPN is one of the representative works that build a feature pyramid by multi-scale features summation. However, the design defects behind prevent the multi-scale features from being fully exploited. In this paper, we begin by first analyzing the design defects of feature pyramid in FPN, and then introduce a new feature pyramid architecture named AugFPN to address these problems. Specifically, AugFPN consists of three components: Consistent Supervision, Residual Feature Augmentation, and Soft RoI Selection. AugFPN narrows the semantic gaps between features of different scales before feature fusion through Consistent Supervision. In feature fusion, ratio-invariant context information is extracted by Residual Feature Augmentation to reduce the information loss of feature map at the highest pyramid level. Finally, Soft RoI Selection is employed to learn a better RoI feature adaptively after feature fusion. By replacing FPN with AugFPN in Faster R-CNN, our models achieve 2.3 and 1.6 points higher Average Precision (AP) when using ResNet50 and MobileNet-v2 as backbone respectively. Furthermore, AugFPN improves RetinaNet by 1.6 points AP and FCOS by 0.9 points AP when using ResNet50 as backbone. Codes are available on https://github.com/Gus-Guo/AugFPN.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/b3e8260558f0ce5c215b3a1751864232f888d048

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Citations in the corpus

FFCA-YOLO for Small Object Detection in Remote Sensing Images (2024, background): “Guo et al. [30] introduce AugFPN to address the inconsistency between detailed and semantic information in feature maps.”
AFPN: Asymptotic Feature Pyramid Network for Object Detection (2023, unknown): “8 Faster R-CNN+AugFPN [11] 800× 1333 38.”
DetectoRS: Detecting Objects with Recursive Feature Pyramid and Switchable Atrous Convolution (2020, unknown): “, [5,7,8,10,26,27, 29,34,62,75].”
YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information (2024, methodology): “In addition, some researchers have tried to develop more general objective functions, such as loss function [5, 45, 46, 50, 77, 78], label assignment [10, 12, 33, 67, 79] and auxiliary supervision [18, 20, 24, 28, 29, 51, 54, 68, 76].”