Résumé: Instance-level perception tasks like object detection, instance segmentation, and 3D detection require many training samples to achieve satisfactory performance. The meticulous labels for these tasks are usually expensive to obtain and data augmentation is a natural choice to tackle such a problem. However, instance-level augmentation is less studied in previous research. In this paper, we present an effective, efficient and unified crop-paste mechanism to augment the training set utilizing existing instance-level annotations. Our design is derived from visual coherence and mines three inherent principles that widely exist in real-world data: (i) background coherence in local neighbor area, (ii) appearance coherence for instance placement, and (iii) instance coherence within the same category. Such methodologies are unified for various tasks including object detection, instance segmentation, and 3D detection. Extensive experiments demonstrate that our proposed approaches can successfully boost the performance of diverse frameworks on various datasets across multiple tasks, without modifying the network structure. Remarkable improvements are obtained: 5.1 mAP for object detection and 3.2 mAP for instance segmentation on COCO dataset, and 6.9 mAP for 3D detection on ScanNetV2 dataset. Our method can be easily integrated into different frameworks without affecting the training and inference efficiency.

Langue: Anglais