Active and semi-supervised learning for object detection with imperfect data

Phill-Kyu Rhee, Enkhbayar Erdenee, Shin Dong Kyun, Minhaz Uddin Ahmed, SongGuo Jin

Abstract: In this paper, we address the combination of the active learning (AL) and semi-supervised (SSL) learnings, called ASSL, to leverage the strong points of the both learning paradigms for improving the performance of object detection. Considering the pros and cons of the AL and SSL learning methods, ASSL where SSL method provides the incremental improvement of semi-supervised detection performance by combining the concept of diversity imported from AL methods. The proposed method demonstrates outstanding performance compared with state-of-art methods on the challenging Caltech pedestrian detection dataset, reducing the miss rate to 12.2%, which is significantly smaller than current state-of-art. In addition, extensive experiments have been carried out using ILSVRC detection dataset and online evaluation for activity recognition.