Coreset Selection for Object Detection
This addresses the challenge of efficiently selecting representative subsets for object detection datasets, which is incremental as it adapts existing coreset methods to a more complex, multi-object scenario.
The paper tackles the problem of coreset selection for object detection, where images contain multiple objects, by introducing CSOD, a method that uses imagewise and classwise feature vectors with submodular optimization, resulting in a +6.4%p improvement in AP$_{50}$ over random selection on the Pascal VOC dataset when selecting 200 images.
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 AP$_{50}$ when selecting 200 images.