We propose a novel attention mechanism to enhance Convolutional Neural Networks for fine-grained recognition. It learns to attend to lower-level feature activations without requiring part annotations and uses these activations to update and rectify the output likelihood distribution. In contrast to other approaches, the proposed mechanism is modular, architecture-independent and efficient both in terms of parameters and computation required. Experiments show that networks augmented with our approach systematically improve their classification accuracy and become more robust to clutter. As a result, Wide Residual Networks augmented with our proposal surpasses the state of the art classification accuracies in CIFAR-10, the Adience gender recognition task, Stanford dogs, and UEC Food-100.

本研究提出了一种新颖的注意力机制，用于增强卷积神经网络进行精细识别，并通过在不需要部件注释的情况下学习参考低层特征激活来更新和纠正输出的概率分布，实验证明，使用我们的方法增强的网络系统地提高了分类准确率和对干扰的稳健性，因此， Wide Residual Networks 自提出建议以来，在 CIFAR-10、Adience 性别识别任务、斯坦福狗和 UEC 食品-100 等方面超越了现有技术的分类精度。

出席与修正：一种用于细粒度恢复的门控注意机制