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

Evaluating Weakly Supervised Object Localization Methods Right