# DMA-Net: Decoupled Multi-Scale Attention for Few-Shot Object Detection
## 2 Related Works
### Few-Shot Object Detection  
Due to the rapid development of FSC tasks, few-shot object detection has begun to make continuous progress. Methods [20; 21; 22; 23] based on metric learning in FSC are most widely used in FSOD tasks. Recent methods of FSOD tasks can be divided into three categories: meta-learning-based, parameter-based, and sample-processing-based methods. Meta-learning-based methods are the most common. Following the N-way K-shot strategy, which is widely used in FSC, these methods typically have two branches: a support branch and a query branch. Support examples and query examples are fed into these two respective branches, keeping the ratio as N\(\times\)K:1 and learning meta-knowledge through a task-based training strategy. The design of the networks causes query images to take support images as the dynamic condition to use to adapt to new tasks quickly. FSRW [24] was proposed based on YOLOv2, which extracts the important information regarding support images as reweighted vectors for each class and achieves reweighted features, using them to query images to obtain more representative features for prediction. Inspired by [24], Meta R-CNN [25] was proposed based on Faster R-CNN [3], which adds weight vectors as points of attention on each region of interest (RoI) feature; however, the weight vectors are fixed during the fine-tuning stage. [PERSON] et al. [27] proposed ONCE based on a center net and implemented incremental learning, which can detect novel classes directly by means of novel support examples without fine-tuning. FSDeView [26] is a more suitable fusion method based on Meta R-CNN [25], allowing the query branch to obtain more representative information from support images. From the perspective of the two key modules of Faster R-CNN [3], FSOD [32] proposed an attention RPN module, allowing the network to consider important support information when filtering foregrounds and backgrounds. [PERSON] et al. [33] used two attention mechanisms based on multihead attention [34] which were easy to plug and play. Inspired by FSOD [32], the studies on DAnA [35] and Meta Faster R-CNN [36] proposed related solutions to the problem of spatial relationships between support features and query features, and both achieved excellent performance. To generate high-quality proposals and make them discriminative, [PERSON] et al. [37] used support query mutual guidance and hybrid loss. In terms of enhancing the classifier and regressor, [PERSON] et al. [38] added a correction network in the support branch to refine the classification scores, and [PERSON] et al. [39] proposed a dynamic classifier and semi-explicit regressor to improve the generalizability. Most networks are based on Faster R-CNN [3]; specifically, the methods in [40; 41] were improved based on DETR [42] and VIT [43]. The parameter-based methods have simple structures compared with other methods. TFA [44] and MetaDet [45] divide the parameters of Faster R-CNN [3] into two parts (category-specific and category-agnostic) with their respective strategies. DeFRCN [46] was proposed from the perspective of solving the two key contradictions of Faster R-CNN [3]. The methods used in [47; 48]based on sample augmentation provide the support branch with richer features in terms of the scale and quantity of input, and those used in [49; 50] process the potential relationship within the data to enhance the information on novel classes. [PERSON] et al. [51] enhanced the features for classification at the spatial, task, and regularization levels. [PERSON] et al. [52] incorporated text-modal descriptions for each category to alleviate confusion regarding the classification of novel classes, and [PERSON] et al. [53] employed category knowledge to guide parameter calibration. The approaches mentioned above used a similar training strategy, i.e., training on base classes to obtain a network with the ability to learn meta-knowledge, and then allowing it to learn the knowledge of novel classes after fine-tuning both novel and base classes.