Few-shot object detection via query feature calibration

Few-Shot object detection is a task that trains a model to effectively recognize and locate novel classes of objects given a very limited amount of labeled samples. However, due to the scarcity of samples for novel classes, the model always lacks sufficient feature discrimination between base classes and the few-shot novel classes, conventional classification methods are prone to confuse similar categories, affecting the accuracy of object detection. In this paper, the query calibration method via graph-centrality-based prototype (GC-PQC) and a Gradual Gradient Isolation (GGI) is proposed. The GC-PQC method constructs intra-class correlation models using graph centrality measures to enhance feature representation and alleviate inter-class confusion. Meanwhile, the GGI module progressively decouples the gradients of backpropagation to promote feature independence during the fine-tuning process. Together, these two methods improve the performance of few-shot object detection models and enhance the feature representation ability of novel classes. Experiment show that our framework achieves a 2%-5% increase in accuracy on novel classes across multiple datasets, demonstrating its effectiveness in addressing the challenges of few-shot object detection.