Portable Active Learning for Object Detection
Researchers have introduced Portable Active Learning (PAL), a novel framework designed to enhance the efficiency and scalability of object detection systems. Addressing the high costs and labor intensity associated with annotating bounding boxes, PAL offers a detector-agnostic solution that operates solely on inference outputs, eliminating the need for modifications to model internals or training pipelines. The framework strategically combines class-wise instance uncertainty with image-level diversity to guide data selection. By training lightweight logistic classifiers to distinguish true from false positives, PAL generates entropy-based uncertainty scores. These are further refined using global image entropy, class diversity, and similarity metrics to select informative and diverse data batches. Extensive experiments conducted on standard datasets, including COCO, PASCAL VOC, and BDD100K, demonstrate that PAL significantly improves label efficiency and detection accuracy compared to existing active learning baselines. This innovation provides a practical, cost-effective approach for deploying scalable object detection in real-world applications, reducing manual annotation efforts while maintaining high performance standards across various detector architectures.
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Portable Active Learning for Object Detection
Researchers have introduced Portable Active Learning (PAL), a novel framework designed to enhance the efficiency and scalability of object detection systems. Addressing the high costs and labor intensity associated with annotating bounding boxes, PAL offers a detector-agnostic solution that operates solely on inference outputs, eliminating the need for modifications to model internals or training pipelines. The framework strategically combines class-wise instance uncertainty with image-level diversity to guide data selection. By training lightweight logistic classifiers to distinguish true from false positives, PAL generates entropy-based uncertainty scores. These are further refined using global image entropy, class diversity, and similarity metrics to select informative and diverse data batches. Extensive experiments conducted on standard datasets, including COCO, PASCAL VOC, and BDD100K, demonstrate that PAL significantly improves label efficiency and detection accuracy compared to existing active learning baselines. This innovation provides a practical, cost-effective approach for deploying scalable object detection in real-world applications, reducing manual annotation efforts while maintaining high performance standards across various detector architectures.
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