FSMT: Few-shot object detection via Multi-Task Decoupled

With the advancement of object detection technology, few-shot object detection (FSOD) has become a research hotspot. Existing methods face two major challenges: base models have limited generalization to unseen categories, especially with limited few-shot data, where the shared feature representation fails to meet the distinct needs of classification and regression tasks; FSOD is susceptible to overfitting during training. To address these issues, this paper proposes a Multi-Task Decoupled Method (MTDM), which enhances the model's generalization to new categories by separating the feature extraction processes for different tasks. Additionally, a dynamic adjustment strategy is adopted, which adaptively modifies the IOU threshold and loss function parameters based on variations in the training data, reducing the risk of overfitting and maximizing the utilization of limited data resources. Experimental results show that the proposed hybrid model performs well on multiple few-shot datasets, effectively overcoming the challenges posed by limited annotated data.