基于多通道视觉注意力的细粒度图像分类

视觉注意力机制在细粒度图像分类中得到了广泛的应用。现有方法多是构建一个注意力权重图对特征进行简单加权处理。对此，本文提出了一种基于可端对端训练的深度神经网络模型实现的多通道视觉注意力机制，首先通过多视觉注意力图描述对应于视觉物体的不同区域，然后提取对应高阶统计特性得到相应的视觉表示。在多个标准的细粒度图像分类测试任务中，基于多通道视觉注意的视觉表示方法均优于近年主流方法。

Fine-Grained Image Classification with Multi-channel Visual Attention

Visual attention mechanism has been commonly used in state-of-the-art fine-grained classification methods in recent years. However, most attention-based image classification systems only apply single-layer or part-specified attention feature, with simple multiplication-based attention applying method, which limits the information provided by the attention. This paper presents a multi-channel visual attention based fine-grained image classification system. Multi-channel attention features are extracted from the image and applied to low-level features, with subtraction of mean values corresponding to each layer of attention for high-order representation, making the model an end-to-end optimizable deep neural network architecture. On multiple commonly used fine-grained classification datasets, the presented method outperforms state-of-the-art methods with a large margin.