Asymptotic Feature Pyramid Network for Labeling Pixels and Regions
Guoyu Yang, Jie Lei, Hao Tian, Zunlei Feng, Ronghua Liang
Abstract
Multi-scale features are crucial in encoding objects with varying scales in vision tasks. The classic top-down and bottom-up feature pyramid networks are a common strategy for multi-scale feature extraction. However, these approaches suffer from the loss or degradation of feature information, which impairs the fusion effect of non-adjacent levels. In this paper, we propose an Asymptotic Feature Pyramid Network (AFPN) that supports direct interaction between non-adjacent levels. AFPN starts by fusing two adjacent low-level features and asymptotic incorporates higher-level features into the fusion process. This fusion way avoids the significant semantic gap between non-adjacent levels. Adaptive spatial fusion operation is further used to mitigate potential multi-object information conflicts during feature fusion at each spatial location. To reduce parameters, computational requirements, and inference speed, we propose a Lightweight Asymptotic Feature Pyramid Network (LightAFPN) that uses the concept of reparametrization. We evaluate the proposed method on the MS-COCO 2017, PASCAL VOC and Cityscapes datasets in both object detection and semantic segmentation frameworks. Experimental evaluation shows that our method achieves more competitive results than other state-of-the-art feature pyramid networks. The code is available at https://github.com/gyyang23/AFPN.