Augmented Box Replay: Overcoming Foreground Shift for Incremental Object Detection
By Liu Yu-yang, Cong Yang, Dipam Goswami, Liu Xialei, Joost van de Weijer. 2023
59 citations recorded by the source corpus.
In incremental learning, replaying stored samples from previous tasks together with current task samples is one of the most efficient approaches to address catastrophic forgetting. However, unlike incremental classification, image replay has not been successfully applied to incremental object detection (IOD). In this paper, we identify the overlooked problem of foreground shift as the main reason for this. Foreground shift only occurs when replaying images of previous tasks and refers to the fact that their background might contain foreground objects of the current task. To overcome this problem, a novel and efficient Augmented Box Replay (ABR) method is developed that only stores and replays foreground objects and thereby circumvents the foreground shift problem. In addition, we propose an innovative Attentive RoI Distillation loss that uses spatial attention from region-of-interest (RoI) features to constrain current model to focus on the most important information from old model. ABR significantly reduces forgetting of previous classes while maintaining high plasticity in current classes. Moreover, it considerably reduces the storage requirements when compared to standard image replay. Comprehensive experiments on Pascal-VOC and COCO datasets support the state-of-the-art performance of our model1.
Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Jun Liu
- Hossein Rahmani
- Bartłomiej Twardowski
- Joost van de Weijer
- Albin Soutif-Cormerais
- Dipam Goswami
- Li Xu
- Bryan M. Williams
In Computer Vision and Pattern Recognition
- Jun Liu
- Hossein Rahmani
- Bartłomiej Twardowski
- Joost van de Weijer
- Albin Soutif-Cormerais
- Dipam Goswami
- Li Xu
- Bryan M. Williams
Citations in the corpus
- Recent Advances of Continual Learning in Computer Vision: An Overview (2021, positive): “They then proposed a method named Augmented Box Replay to handle this problem, and a loss function named attentive Region-of-Interest distillation loss to further reduce catastrophic forgetting.”
- Resurrecting Old Classes with New Data for Exemplar-Free Continual Learning (2024, methodology): “Exemplar-based CIL methods [2, 3, 6, 8, 13, 25, 38, 49] store small subsets of data from each task.”