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

arXiv: https://arxiv.org/abs/2007.04178 · HTML: https://mlsanity.com/paper/2007.04178/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Jul 2020.

## Built on by (most cited first, 19 of 19)

- [Bridging the Gap between Classification and Localization for Weakly Supervised Object Localization](https://mlsanity.com/paper/2204.00220/) (2022)
- [Keep CALM and Improve Visual Feature Attribution](https://mlsanity.com/paper/2106.07861/) (2021)
- [BAH Dataset for Ambivalence/Hesitancy Recognition in Videos for Digital Behavioural Change](https://mlsanity.com/paper/2505.19328/) (2025)
- [ViTOL: Vision Transformer for Weakly Supervised Object Localization](https://mlsanity.com/paper/2204.06772/) (2022)
- [Anti-Adversarially Manipulated Attributions for Weakly Supervised Semantic Segmentation and Object Localization](https://mlsanity.com/paper/2204.04890/) (2022)
- [On Label Granularity and Object Localization](https://mlsanity.com/paper/2207.10225/) (2022)
- [Re-Attention Transformer for Weakly Supervised Object Localization](https://mlsanity.com/paper/2208.01838/) (2022)
- [Attention, Please! Revisiting Attentive Probing Through the Lens of Efficiency](https://mlsanity.com/paper/2506.10178/) (2025)
- [Three Recipes for Better 3D Pseudo-GTs of 3D Human Mesh Estimation in the Wild](https://mlsanity.com/paper/2304.04875/) (2023)
- [VLM's Eye Examination: Instruct and Inspect Visual Competency of Vision Language Models](https://mlsanity.com/paper/2409.14759/) (2024)
- [Few-shot Font Generation with Weakly Supervised Localized Representations](https://mlsanity.com/paper/2112.11895/) (2021)
- [Similarity of Neural Architectures using Adversarial Attack Transferability](https://mlsanity.com/paper/2210.11407/) (2022)
- [MinMaxCAM: Improving object coverage for CAM-basedWeakly Supervised Object Localization](https://mlsanity.com/paper/2104.14375/) (2021)
- [Visually Grounded Keyword Detection and Localisation for Low-Resource Languages](https://mlsanity.com/paper/2302.00765/) (2023)
- [Boosting Weakly-Supervised Image Segmentation via Representation, Transform, and Compensator](https://mlsanity.com/paper/2309.00871/) (2023)
- [Multiple Instance Verification](https://mlsanity.com/paper/2407.06544/) (2024)
- [BroadCAM: Outcome-agnostic Class Activation Mapping for Small-scale Weakly Supervised Applications](https://mlsanity.com/paper/2309.03509/) (2023)
- [Fine-Grained Attention for Weakly Supervised Object Localization](https://mlsanity.com/paper/2104.04952/) (2021)
- [Categorical Knowledge Fused Recognition: Fusing Hierarchical Knowledge with Image Classification through Aligning and Deep Metric Learning](https://mlsanity.com/paper/2407.20600/) (2024)

## Abstract (arXiv)

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.