Incremental Few Shot Object Detection

Incremental few-shot object detection (iFSD) focuses on training object detectors to recognize new object classes using only a few examples, without losing the ability to detect previously learned classes. Current research emphasizes fine-tuning approaches, often incorporating techniques like prototype generation, contrastive learning, and self-supervised learning to mitigate catastrophic forgetting and improve performance on novel classes. These advancements are significant because they address the limitations of traditional object detection methods in scenarios with limited data, paving the way for more adaptable and robust computer vision systems in real-world applications.