Incremental Object Detection

Incremental object detection (IOD) focuses on training object detectors that can learn new object classes sequentially without forgetting previously learned ones, a crucial challenge in real-world applications with evolving object categories. Current research heavily utilizes transformer-based architectures and explores techniques like knowledge distillation, exemplar replay, and pseudo-labeling to mitigate "catastrophic forgetting," often incorporating generative models to augment training data. This field is significant because it enables the development of more robust and adaptable object detection systems for applications like autonomous driving and robotics, where continuous learning is essential.