Localization Uncertainty-Based Attention for Object Detection
This work addresses the need for reliable confidence estimates in object detection for real-world applications, representing an incremental improvement over existing methods.
The paper tackles the problem of providing confidence in object detection results by proposing an uncertainty-aware dense detector (UADET) that predicts localization uncertainties and uses an uncertainty attention module (UAM) for feature refinement, achieving a state-of-the-art AP of 48.3% on COCO test-dev.
Object detection has been applied in a wide variety of real world scenarios, so detection algorithms must provide confidence in the results to ensure that appropriate decisions can be made based on their results. Accordingly, several studies have investigated the probabilistic confidence of bounding box regression. However, such approaches have been restricted to anchor-based detectors, which use box confidence values as additional screening scores during non-maximum suppression (NMS) procedures. In this paper, we propose a more efficient uncertainty-aware dense detector (UADET) that predicts four-directional localization uncertainties via Gaussian modeling. Furthermore, a simple uncertainty attention module (UAM) that exploits box confidence maps is proposed to improve performance through feature refinement. Experiments using the MS COCO benchmark show that our UADET consistently surpasses baseline FCOS, and that our best model, ResNext-64x4d-101-DCN, obtains a single model, single-scale AP of 48.3% on COCO test-dev, thus achieving the state-of-the-art among various object detectors.