Replay-Based Incremental Object Detection With Local Response Exploration

Incremental object detection (IOD) aims to train an object detector on non-stationary data streams without forgetting previous knowledge. Prevalent replay-based methods keep a buffer composed of carefully selected instances towards this goal. However, due to the limited storage space and uniform feature distribution, existing methods are prone to overfit on replayed instances, leading to poor generalization on diverse test data. Additionally, the imbalance in data quantity makes the detector fail to distinguish old and new classes that are visually similar, introducing bias toward new classes. To enhance the diversity of stored instances and eliminate bias, we propose a Local Response Exploration (LRE) framework, which comprises three modules. First, Region-Entropy Instance Selector (REIS) introduces a novel metric to assess instance diversity based on the entropy of local responses. Second, Confusion-Guided Instance Replay (CGIR) replaces the previous random replay approach by replaying specific old class instances based on class similarity, ensuring that parameters for similar new and old classes are updated together, thereby mitigating bias and helping mining discriminative patterns. Third, Confusion-Aware Region Segregation (CARS) adaptively differentiates biased regions from other regions based on local responses, reducing bias toward new classes while preserving relationships between new and old classes. Extensive evaluations on Pascal-VOC and MS COCO datasets demonstrate that our approach outperforms State-of-the-Art methods in incremental object detection.