  Weakly-supervised object localization (WSOL) has gained popularity over the
last years for its promise to train localization models with only image-level
labels. Since the seminal WSOL work of class activation mapping (CAM), the
field has focused on how to expand the attention regions to cover objects more
broadly and localize them better. However, these strategies rely on full
localization supervision to validate hyperparameters and for model selection,
which is in principle prohibited under the WSOL setup. In this paper, we argue
that WSOL task is ill-posed with only image-level labels, and propose a new
evaluation protocol where full supervision is limited to only a small held-out
set not overlapping with the test set. We observe that, under our protocol, the
five most recent WSOL methods have not made a major improvement over the CAM
baseline. Moreover, we report that existing WSOL methods have not reached the
few-shot learning baseline, where the full-supervision at validation time is
used for model training instead. Based on our findings, we discuss some future
directions for WSOL.
