  Curating an informative and representative dataset is essential for enhancing
the performance of 2D object detectors. We present a novel active learning
sampling strategy that addresses both the informativeness and diversity of the
selections. Our strategy integrates uncertainty and diversity-based selection
principles into a joint selection objective by measuring the collective
information score of the selected samples. Specifically, our proposed NORIS
algorithm quantifies the impact of training with a sample on the
informativeness of other similar samples. By exclusively selecting samples that
are simultaneously informative and distant from other highly informative
samples, we effectively avoid redundancy while maintaining a high level of
informativeness. Moreover, instead of utilizing whole image features to
calculate distances between samples, we leverage features extracted from
detected object regions within images to define object features. This allows us
to construct a dataset encompassing diverse object types, shapes, and angles.
Extensive experiments on object detection and image classification tasks
demonstrate the effectiveness of our strategy over the state-of-the-art
baselines. Specifically, our selection strategy achieves a 20% and 30%
reduction in labeling costs compared to random selection for PASCAL-VOC and
KITTI, respectively.
