Abstract
Despite recent advances in machine learning, it is still challenging to realize real-time and accurate detection in images. The recently proposed StairNet detector (Sanghyun et al. WACV 2018), one of the strongest one-stage detectors, tackles this issue by using a SSD in conjunction with a top-down enrichment module. However, the StairNet approach misses the finer localization information which can be obtained from the lower layer and lacks a feature selection mechanism, which can lead to suboptimal features during the merging step. In this paper, we propose what is termed the gated bidirectional feature pyramid network (GBFPN), a simple and effective architecture that provides a significant improvement over the baseline model, StairNet. The overall network is composed of three parts: a bottom-up pathway, a top-down pathway, and a gating module. Given the multi-scale feature pyramid of deep convolutional network, two separate pathways introduce both finer localization cues and high-level semantics. In each pathway, the gating module dynamically re-weights the features before the combining step, transmitting only the informative features. Placing GBFPN on top of a basic one-stage detector SSD, our method shows state-of-the-art results.