论文标题
发掘水下对象检测的ROI注意力
Excavating RoI Attention for Underwater Object Detection
论文作者
论文摘要
自我注意力是深度学习中最成功的设计之一，它可以根据注意矩阵计算不同令牌的相似性并重建该功能。自我注意力最初是为NLP设计的，在计算机视觉中也很受欢迎，可以分为像素级的关注和贴片级别的关注。在对象检测中，ROI功能可以看作是基本特征图的补丁。本文旨在将注意模块应用于ROI功能以提高性能。我们选择了外部注意模块，而不是采用原始的自我发场模块，这是一个修改后的自我注意力，其参数减少了。借助提出的双头结构和位置编码模块，我们的方法可以在对象检测中实现有希望的性能。综合实验表明，它可以实现有希望的性能，尤其是在水下对象检测数据集中。该代码将在：https：//github.com/zsyasd/excavating-roi-prestition-for-under-underwater-object中
Self-attention is one of the most successful designs in deep learning, which calculates the similarity of different tokens and reconstructs the feature based on the attention matrix. Originally designed for NLP, self-attention is also popular in computer vision, and can be categorized into pixel-level attention and patch-level attention. In object detection, RoI features can be seen as patches from base feature maps. This paper aims to apply the attention module to RoI features to improve performance. Instead of employing an original self-attention module, we choose the external attention module, a modified self-attention with reduced parameters. With the proposed double head structure and the Positional Encoding module, our method can achieve promising performance in object detection. The comprehensive experiments show that it achieves promising performance, especially in the underwater object detection dataset. The code will be avaiable in: https://github.com/zsyasd/Excavating-RoI-Attention-for-Underwater-Object-Detection
