论文标题
评估弱监督对象本地化：协议，指标和数据集
Evaluation for Weakly Supervised Object Localization: Protocol, Metrics, and Datasets
论文作者
论文摘要
弱监督的对象本地化（WSOL）在过去几年中因其承诺仅使用图像级标签训练本地化模型而越来越受欢迎。由于类激活映射的开创性WSOL工作（CAM），该领域一直集中在如何扩展注意区域以更广泛地覆盖对象并更好地定位它们。但是，这些策略依赖于验证超参数和模型选择的完整本地化监督，在WSOL设置下，这原则上是禁止的。在本文中，我们认为WSOL任务仅包含图像级标签，并提出了一个新的评估协议，其中全面监督仅限于仅与测试集重叠的小型保留集。我们观察到，根据我们的协议，最近的五种WSOL方法并未对CAM基线取得重大改进。此外，我们报告说，现有的WSOL方法尚未达到少量学习基线，在该基线中，验证时间的全套件用于模型培训。根据我们的发现，我们讨论了WSOL的一些未来方向。
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.
