New protocol proposed for realistic evaluation of weakly supervised object localization

Researchers have proposed a new protocol for evaluating Weakly Supervised Object Localization (WSOL) that aims to be more realistic by not requiring bounding box annotations during training or testing. Current WSOL methods often rely on these annotations for hyper-parameter tuning and threshold estimation, which are typically unavailable in real-world scenarios. The proposed protocol generates noisy pseudo-boxes using methods like Selective Search or CLIP for model selection and threshold estimation, demonstrating comparable performance to methods using ground truth bounding boxes. AI

IMPACT This new protocol could lead to more accurate and practical development of object localization models in real-world applications.

RANK_REASON The cluster contains an academic paper detailing a new evaluation protocol for a machine learning task. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

• selective search
• Shakeeb Murtaza
• Weakly Supervised Object Localization with Multi-Fold Multiple Instance Learning

• paper
• other

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