Coreset Selection for Object Detection

Hojun Lee, Suyoung Kim, Junhoo Lee, Jaeyoung Yoo, Nojun Kwak; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2024, pp. 7682-7691

Coreset selection is a method for selecting a small representative subset of an entire dataset. It has been primarily researched in image classification assuming there is only one object per image. However coreset selection for object detection is more challenging as an image can contain multiple objects. As a result much research has yet to be done on this topic. Therefore we introduce a new approach Coreset Selection for Object Detection (CSOD). CSOD generates imagewise and classwise representative feature vectors for multiple objects of the same class within each image. Subsequently we adopt submodular optimization for considering both representativeness and diversity and utilize the representative vectors in the submodular optimization process to select a subset. When we evaluated CSOD on the Pascal VOC dataset CSOD outperformed random selection by +6.4%p in AP50 when selecting 200 images.