Latent Feature Pyramid Network for Object Detection

Abstract

Object detection methods based on Convolution Neural Networks (CNN) usually utilize feature pyramid networks to detect objects with various scales. The state-of-the-art feature pyramid networks improve detection accuracy by enhancing multi-level feature representations. Fusing multi-level features is the most effective manner to enhance feature representations. However, the existing feature pyramid networks usually fuse multi-level features by element-wise operations. It leads to the lack of long-range dependencies in the feature fusion. To address this problem, we propose a simple yet efficient feature pyramid network named latent feature pyramid network (LFPN). LFPN can enhance feature representations by modeling inner-scale and cross-scale long-range dependencies through conducting inner-scale and cross-scale feature fusion in the latent space. Comprehensive experiments are performed on two challenge object detection datasets: MS COCO and Pascal VOC. The experimental results show consistent improvements on various feature pyramid networks, backbones, and object detectors, which demonstrates the effectiveness and generality of our LFPN. Our code and models will be made publicly available.