# DMA-Net: Decoupled Multi-Scale Attention for Few-Shot Object Detection
###### Abstract  
As one of the most important fields in computer vision, object detection has undergone marked development in recent years. Generally, object detection requires many labeled samples for training, but it is not easy to collect and label samples in many specialized fields. In the case of few samples, general detectors typically exhibit overfitting and poor generalizability when recognizing unknown objects, and many FSOD methods also cannot make good use of support information or manage the potential problem of information relationships between the support branch and the query branch. To address this issue, we propose in this paper a novel framework called Decoupled Multi-scale Attention (DMA-Net), the core of which is the Decoupled Multi-scale Attention Module (DMAM), which consists of three primary parts: a multi-scale feature extractor, a multi-scale attention module, and a decoupled gradient module (DGM). DMAM performs multi-scale feature extraction and layer-to-layer information fusion, which can use support information more efficiently, and DGM can reduce the impact of potential optimization information exchange between two branches. DMA-Net can implement incremental FSOD, which is suitable for practical applications. Extensive experimental results demonstrate that DMA-Net has comparable results on generic FSOD benchmarks, particularly in the incremental FSOD setting, where it achieves a state-of-the-art performance. object detection; few-shot learning; incremental learning; meta learning +
Footnote †: journal:  
information from the support branch is currently the most challenging task. Based on the widely used object detector Faster R-CNN [3], support information is used in the detection module in [25,26], but the region proposal network (RPN) is also an important part of the network. The scarcity of samples affects the foreground and background classification of the RPN, which in turn generates unsuitable proposals and affects the subsequent detection of objects. Therefore, support information was added into the query branch before RPN in [28], but was only a simple fusion of single-size features. Overall, these methods only make superficial use of support information. When sample information is scarce, it is necessary to fully utilize all aspects of support information and to consider the location and form of fusion to make full use of support information. In addition, two branch models may encounter issues of inconsistency between gradient and parameter types during parameter updates. Specifically, the RPN only classifies the foreground and background, as it is a class-agnostic module, while the RCNN must identify the category of objects, as it is a class-specific module. The support branch is also class-specific; thus, there may be a problem of unnecessary optimization information exchange between the support branch and the query branch in the models that add support information before RPN. To manage the relationship between the support branch and the query branch more effectively, and to obtain more information from the support branch while reducing the interference between the two branches, we propose the decoupled multi-scale attention (DMA-Net) method. Many existing methods require a fine-tuning stage and cannot directly recognize novel objects, while the proposed method can achieve good performance for both novel and base classes without fine-tuning, making it suitable for practical applications. To the best of our knowledge, the technique of decoupling gradients between the two branches prior to RPN has not been employed in previous FSOD models. Therefore, this study represents a pioneering effort to implement the decoupled multi-scale attention approach in FSOD models. Our source code for this method is publicly available on GitHub at: [[https://github.com/xijunxie/DMA-Net](https://github.com/xijunxie/DMA-Net)]([https://github.com/xijunxie/DMA-Net](https://github.com/xijunxie/DMA-Net)). Figure 1: N-way K-shot training strategy based on meta learning.