Abstract
Weakly supervised object detection (WSOD) is an important issue in vision tasks. Unlike fully supervised learning, weakly supervised learning uses only image-level labels without bounding boxes. Training with only image-level labels makes it difficult to train deep networks based detectors in a weakly supervised manner. This paper proposes methods which are denoted as refining pseudo ground truth (RPG) and selecting pseudo ground truth (SPG), respectively. Pseudo ground truth for 1 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">st</sup> instance classifier refinement network is refined to make better bounding boxes with RPG and select good bounding boxes with SPG. The proposed methods obtain 55.75% as a mean average precision (mAP) on VOC 2007 that outperforms the previous state-of-the-art.
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