基于注意力机制的弱监督细粒度图像分类

针对细粒度图像分类任务中难以对图中具有鉴别性对象进行有效学习的问题,本文提出了一种基于注意力机制的弱监督细粒度图像分类算法.该算法能有效定位和识别细粒度图像中语义敏感特征.首先在经典卷积神经网络的基础上通过线性融合特征得到对象整体信息的表达,然后通过视觉注意力机制进一步提取特征中具有鉴别性的细节部分,获得更完善的细粒度特征表达.所提算法实现了线性融合和注意力机制的结合,可看作是多网络分支合作训练共同优化的网络模型,从而让网络模型对整体信息和局部信息都有更好的表达能力.在3个公开可用的细粒度识别数据集上进行了验证,实验结果表明,所提方法有效性均优于基线方法,且达到了目前先进的分类水平.

Weakly Supervised Fine-Grained Image Classification Based on Attention Mechanism

Fine-grained image classification is challenging due to the difficulty in the effective learning of discriminative objects in images. Therefore, this study proposes a weakly supervised fine-grained image classification algorithm based on the attention mechanism. This algorithm can accurately locate and identify the semantically sensitive features in fine-grained images. First, on the basis of the classic convolutional neural network, the overall information of an object can be expressed by the linear fusion of features. Then, the discriminative details of the features are further extracted through the visual attention mechanism to obtain a more complete fine-grained feature expression. The proposed algorithm combines linear fusion with the attention mechanism and it can be regarded as a network model of multi-network-branch cooperative training and joint optimization. Thus, the network model can better express the overall and local information. Experiments on three publicly available fine-grained identification datasets show that the proposed method is superior to the baseline method and achieves the advanced classification level.