OLN++: Improved Object Localization Network for Open-world Object Detection

Abstract

Open-world object detection (OWOD) is vital for identifying the new objects not encountered during training. Among the various methods for OWOD, Object Proposals without Learning Classification (OPwLC) stands out, with its Object Localization Network (OLN) stressing the localization features. However, OLN overlooks classification features, leading OPwLC to identify parts of a single object as multiple objects mistakenly. Inspired by the non-maximum suppression (NMS) technique, known for eliminating low-confidence detections, we sought to integrate NMS into OPwLC. However, direct integration of NMS into OPwLC presents a challenge, as OLN does not generate classification confidence scores, which are critical for applying NMS. To address this limitation, we developed a confidence measure module and proposed OLN++, filling the confidence scores gap. OLN++ can be easily implemented with just a few fully connected layers. We evaluated the effectiveness of OLN++ using NMS, Soft-NMS, and the Weighted Box Fusion variant on open-world detection tasks. Experimental results demonstrate that OLN++ significantly outperforms the original OLN.