面向细粒度图像识别的通道注意力多分支网络

Channel Attention Multi-Branch Network for Fine-Grained Image Recognition

细粒度图像识别研究的内容是大类下的子类别识别问题,其关键是找到图像中的关键区域并从中提取有效特征。针对现有方法在定位关键区域时无法兼顾准确性和计算量的问题,提出了一种引入高效通道注意力模块的多分支网络。首先,在递归注意力卷积神经网络的基础上引入通道注意力定位图像中目标的位置。然后,用深度超参数化卷积替换传统卷积操作,增加了网络可学习的参数。最后,用改进的注意力部件模块切割出多个图像关键区域部件,以捕捉丰富的局部信息。实验结果表明,本方法在弱监督情况下的识别效果较好,在两个常用细粒度数据集Stanford Cars、Food-101上的识别准确率分别为95.4%和90.6%。

The content of fine-grained image recognition research is the problem of sub-category recognition under broad categories.The key is to find the key regions in the image and extract effective features from them.Aiming at the problem that the existing methods cannot balance the accuracy and the amount of calculation when locating key areas,a multi-branch network that introduces an efficient channel attention module is proposed in this paper.First,the channel attention is introduced on the basis of the recurrent attention convolutional neural network to locate the target position in the image.Then,the traditional convolution operation is replaced with depthwise over-parameterized convolution,which increases the parameters that the network can learn.Finally,the advanced attention part module is used to cut out multiple image key area components to capture rich local information.Experimental results show that the method has a better recognition effect in weakly supervised situations,and the recognition accuracy rates on the two commonly used fine-grained datasets Stanford Cars and Food-101 are 95.4%and 90.6%,respectively.