Evaluating Weakly Supervised Object Localization Methods Right (2001.07437v2)

Abstract: 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.

• The paper introduces a standardized evaluation protocol by limiting full supervision to a small, held-out validation set.
• The paper presents robust, threshold-independent metrics (MaxBoxAcc and PxAP) that show minimal gains over the CAM baseline.
• The study finds that few-shot learning baselines often outperform WSOL methods, suggesting a shift toward hybrid supervision.

Evaluating Weakly Supervised Object Localization Methods

The paper, “Evaluating Weakly Supervised Object Localization Methods Right,” critically examines existing methodologies used in Weakly-Supervised Object Localization (WSOL), a field that has garnered attention for training models with mere image-level labels. Since class activation mapping (CAM) introduced this domain, many methods have emerged aiming to enhance object localization accuracy by broadening focus from discriminative object parts to comprehensive object coverage.

Key Contributions and Observations

1. Ill-posed Problem and New Protocol: The authors assert that the WSOL task is inherently ill-posed when relying solely on image-level labels, as existing strategies indirectly benefit from full supervision during validation—contradicting the WSOL’s inherent focus on weak supervision. To resolve this, they propose limiting full supervision to a small, held-out validation set and standardizing the evaluation protocol.
2. Evaluation Metrics and Results: They introduce new metrics, MaxBoxAcc and PxAP, which provide a more robust and threshold-independent evaluation of WSOL methods. The authors demonstrate that when these uniform metrics are applied, no substantial improvements were observed in recent WSOL methods compared to the CAM baseline. For instance, on the ImageNet dataset, performance improvements range insignificantly or even regress for some methods, highlighting that claimed advancements may heavily depend on chosen calibration and thresholds.
3. Few-Shot Learning and Baselines: A significant finding is that few-shot learning (FSL) baselines, which utilize a few fully supervised samples directly for model training, often outperform existing WSOL methods. This suggests that a shift in focus towards incorporating limited full supervision might yield more practical results, adhering to the resources available in real-case scenarios rather than striving for purely weak supervision.
4. Hyperparameter Sensitivity: WSOL methods exhibit sensitivity to hyperparameters, which indirectly need full supervision for effective tuning. This highlights the pseudo-supervised nature of such techniques when they are intended to function under weak supervision paradigms.

Implications and Future Directions

• Reassessing WSOL Objectives:

The paper challenges researchers to rethink WSOL’s foundational structure, advocating for semi-weakly supervised paradigms that balance weak and full supervision to resolve the ill-posed nature of the problem.

• Data-centric Approaches:

To combat the inherent ill-posed challenges, the authors propose augmenting datasets with critical foreground and background cues that have been underrepresented historically. This strategy could potentially make the WSOL more tractable and extend its practical applicability.

• Extended Applicability:

The research emphasizes that these insights might extend to other domains reliant on weak or scaled-down supervision such as zero-shot learning or unsupervised learning, where implicit full supervision often creeps into model evaluation.

In conclusion, this paper invites a fundamental re-evaluation of WSOL methodologies, encouraging the field to incorporate limited supervision strategically and establish more rigorous, standardized evaluation protocols. It sets the stage for the next generation of weak supervision research, aimed at leveraging hybrid learning paradigms for robust performance in real-world applications.