Papers › Evaluation for Weakly Supervised Object Localization: Protocol, Metrics, and Datasets

Evaluation for Weakly Supervised Object Localization: Protocol, Metrics, and Datasets

8 Jul 2020arXiv:2007.04178archive 2025-07-28

Junsuk Choe, Seong Joon Oh, Sanghyuk Chun, Seungho Lee, Zeynep Akata, Hyunjung Shim

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 for validating hyperparameters and 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.

PaperPDFCode

In Syntology Open this paper in Syntology's Atlas, the map of the papers in Syntology's graph and their citations.

Code

clovaai/wsolevaluation officialmentioned in papermentioned on GitHubpytorch report
zphang/saliency_investigation mentioned on GitHubpytorchBSD-3-Clause report

Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.

Code Syntology ran Syntology

Not run by Syntology. Nothing on this page verifies that the listed code works.

Tasks

Few-Shot LearningModel SelectionObject LocalizationWeakly-Supervised Object Localization

Results from the paper archive 2025-07-28

No leaderboard rows for this paper in the archive.

Methods

CAM

Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections