From object difficulty to image scoring: A strategy for active learning in object detection

Abstract (excerpt). Object detection (OD) faces the costly hurdle of large-scale dataset annotation. Active Learning (AL) is a promising solution that selects the most beneficial samples for annotation. Applying AL to OD is challenging, as it requires addressing uncertainties in both classifying and localizing objects while combining object-level information into a single image-level decision. Current methods, like uncertainty sampling, often rely on output-level uncertainty or heuristic signal combinations. However, relying on static model outputs limits their ability to capture underlying feature-space instabilities. To address these limitations, we propose Feature Difficulty-based AL (FDAL), a novel framework that shifts the focus from output-level heuristics to active latent-space manipulation. By systematically interpolating unlabeled features toward class anchors, FDAL identifies prediction inconsistencies to reveal hidden traits unknown to …

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Recommended citation: Duc Tai Phan, Nhut Minh Nguyen, Khang Phuc Nguyen, Phuong-Nam Tran, Nhat Truong Pham, Linh Le, Choong Seon Hong, Duc Ngoc Minh Dang. (2026). "From object difficulty to image scoring: A strategy for active learning in object detection." Knowledge-Based Systems, 115946.
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