Selective Multi-Scale Learning for Object Detection
This work addresses a bottleneck in object detection for computer vision applications, offering incremental improvements to existing detectors.
The paper tackled the problem of multi-scale object detection by proposing a selective multi-scale learning (SMSL) architecture for feature pyramid networks, which improved detection performance with RetinaNet achieving a 1.8% AP increase on COCO.
Pyramidal networks are standard methods for multi-scale object detection. Current researches on feature pyramid networks usually adopt layer connections to collect features from certain levels of the feature hierarchy, and do not consider the significant differences among them. We propose a better architecture of feature pyramid networks, named selective multi-scale learning (SMSL), to address this issue. SMSL is efficient and general, which can be integrated in both single-stage and two-stage detectors to boost detection performance, with nearly no extra inference cost. RetinaNet combined with SMSL obtains 1.8\% improvement in AP (from 39.1\% to 40.9\%) on COCO dataset. When integrated with SMSL, two-stage detectors can get around 1.0\% improvement in AP.