Small object matters in weakly supervised object localization

Weakly-supervised object localization (WSOL) methods aim to capture the extent of the target object without full supervision such as bounding boxes or segmentation masks. Although numerous studies have been conducted in the research field of WSOL, we find that most existing methods are less effective at localizing small objects. In this paper, we first analyze why previous studies have overlooked this problem. Based on the analysis, we propose two remedies: (1) new evaluation metrics and a dataset to accurately measure localization performance for small objects, and (2) a novel consistency learning framework to zoom in on small objects so the model can perceive them more clearly. Our extensive experimental results demonstrate that the proposed method significantly improves small object localization on four different backbone networks and four different datasets, without sacrificing the performance of medium and large objects. In addition to these gains, our method can be easily applied to existing WSOL methods as it does not require any changes to the model architecture or data input pipeline. Code is available at https://github.com/dongjunhwang/small_object_wsol.