Object Recognition Consistency in Regression for Active Detection

Active learning has achieved great success in image classification because of selecting the most informative samples for data labeling and model training. However, the potential of active learning has been far from being realised in object detection due to its unique challenge in utilizing localization information. A popular compromise is to simply take active classification learning over detected object candidates. To consider the localization information of object detection, current effort usually falls into the model-dependent fashion, which either works on specific detection frameworks or relies on additionally designed modules. In this paper, we propose model-agnostic Object Recognition Consistency in Regression (ORCR), which can holistically measure the uncertainty information of classification and localization of each detected candidate from object detection. The philosophy behind ORCR is to obtain the detection uncertainty by calculating the classification consistency through localization regression at two successive detection scales. In the light of the proposed ORCR, we devise an active learning framework that enables an effortless deployment to any object detection architecture. Experimental results on the PASCAL VOC and MS-COCO benchmarks show that our method achieves better performance while simplifying the active detection process.