Fine-grained Image Classification by Integrating Object Localization and Heterogeneous Local Interactive Learning
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摘要: 由于细粒度图像之间存在小的类间方差和大的类内差异, 现有分类算法仅仅聚焦于单张图像显著的局部特征的提取与表示学习, 忽视了多张图像之间局部的异构语义判别信息, 较难关注到区分不同类别的微小细节, 导致学习到的特征缺乏足够区分度. 本文提出了一种渐进式网络以弱监督的方式学习图像不同粒度层级的信息, 首先构建一个注意力累计目标定位模块(Attention accumulation object location module, AAOLM), 在单张图像上从不同的训练轮次和特征提取阶段对注意力信息进行语义目标集成定位. 其次, 设计一个多张图像异构局部交互图模块(Heterogeneous local interaction graph module, HLIGM), 提取每张图像的显著性局部区域特征, 在类别标签引导下构建多张图像的局部区域特征之间的图网络, 聚合局部特征增强表示的判别力. 最后, 利用知识蒸馏将异构局部交互图模块产生的优化信息反馈给主干网络, 从而能够直接提取具有较强区分度的特征, 避免了在测试阶段建图的计算开销. 通过在多个数据集上进行的实验, 证明了提出的方法的有效性, 能够提高细粒度分类的精度.Abstract: Due to the existence of small inter-class differences and large intra-class variance among fine-grained images, the existing classification algorithms only focus on the extraction and representation learning of salient local features of a single image, ignoring the local heterogeneous semantic discrimination information between multiple images, resulting in the lack of sufficient discrimination of the learned features. This paper proposes a progressive network to learn the information of different granularity levels of the image in a weakly supervised manner. First, attention accumulation object location module(AAOLM) is constructed to perform semantic target integration location on attention information from different training epochs and feature extraction stages on a single image. Second, a multi-image heterogeneous local interaction graph module(HLIGM) is designed to construct a graph network and aggregate information between the local region features of multiple images under the guidance of the category label after extracting the saliency local region of each image to enhance the discriminative power of the representation. Finally, the optimization information generated by HLIGM is fed back to the backbone by using knowledge distillation so that it can directly extract features with strong discrimination, avoiding the computational overhead of building the graph in the test phase. Through experiments on multiple data sets, it proves the effectiveness of the proposed method, which can improve the fine-grained classification accuracy.
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图 1 异构局部交互图模块说明图, 细粒度数据集图像由于目标只存在局部的细微不同, 依靠单个图像学习到的特征较难判别. 异构局部交互图模块对不同图像间的局部关系进行显示地对比区分, 并且聚合相互之间的信息获得更有区分度的特征表示
Fig. 1 Illustration of HLIGM, because the objects of fine-grained image datasets are only slightly different locally, it is difficult to learn discriminative features by relying on a single image. HLIGM distinguishes the local relationships explicitly between different images and aggregate information each other to obtain a more discriminative feature representation
图 2 (a, b)使用固定大小的检测框直接在原图中采样有用的目标局部部件, 没有很好地区分开不同的部位并且包含了更多无关的背景信息. (c, d) 展示了定位到目标后放大到一定的尺度再进行部件采样的效果
Fig. 2 (a, b) shows that using the fixed-size anchor directly samples useful local patches of the object in the original image, which does not distinguish different patches well and contains more irrelevant background information. (c, d) shows the effect of patch sampling that it is zoomed in to a certain scale after the object is located
图 3 模型的基本框架图, 该模型分为三个分支, 分别是全局流、目标流和部件流. 在全局流中提取原始图像的特征, 通过注意力累计目标定位模块获取目标的位置裁剪得到目标图像, 然后再在目标流中从不同anchor产生的候选块中根据得分选择显著块作为最终采样到的局部部件, 而部件流通过异构局部交互图学习这些局部部件之间的语义关系提取到更具有区分度的特征
Fig. 3 The basic framework diagram of the model. The model includes three branches, namely global stream, object stream and part stream. The features of the original image are extracted from the global stream, and the position of the object is obtained by AAOLM to crop the object. In object stream, the discriminative patches are selected according to the scores from the default patches generated by different anchors as the final local parts. Part stream generate more discriminative features by learning semantic relationship between these local parts in HLIGM
图 4 异构局部交互图模块结构. 将输入图像的局部部件特征向量作为图的节点表示, 首先对节点表示先进行线性变换, 再将变换后的任意表示两两间进行拼接通过一个单前馈层和非线性激活函数映射为实数作为图的边值反映节点间的关联度, 对各个节点的边值进行softmax得到一个可比较的注意力矩阵. 图中注意力矩阵暗的部分表示不同类别的局部区域节点之间的注意力, 而亮的部分则表示属于相同类别之间的注意力, 对它们计算相应的注意力正则化损失来约束其注意力权值. 对节点的邻居表示根据它们之间的注意力权值加权求和并通过残差学习来更新节点原始的表示
Fig. 4 The structure of HLIGM. It takes the feature vectors of local parts of the input image as node representations in the graph. Firstly, a linear transformation is applied to the node representations. Then, any two transformed representations are concatenated and passed through a single feedforward layer combined with a non-linear activation function to map them into real numbers, serving as edge values in the graph to reflect the degree of association between nodes. A softmax operation is performed on the edge values of every node to obtain a comparable attention matrix. In this matrix, the darker areas indicate the attention between nodes of local regions of different categories, while the brighter areas represent the attention between nodes of the same category. Corresponding attention regularization losses are calculated for them to constrain their attention weights. The representations of neighboring nodes are then aggregated according to their attention-weighted sum, and through residual learning, the original representation of the nodes is updated
图 5 使用CAM和本文AAOLM的峰值响应图的可视化结果. (a) 原始图像. (b) CAM生成的热力图. (c) AAOLM在Resnet-50的$ Conv_{5b} $卷积块输出特征上的注意力图. (d) AAOLM在Resnet-50的$ Conv_{5c} $卷积块输出特征上的注意力图
Fig. 5 Visualization results of peak response maps using CAM and AAOLM in this paper. (a) Original image. (b) Heat map generated by CAM. (c) Attention map of $ Conv_{5b} $ convolution block of Resnet-50 by AAOLM. (d) The attention map of the $ Conv_{5c} $ convolution block of Resnet-50 by AAOLM
图 6 (a) (b) 通过t-SNE可视化部件流主干网络输出特征的聚类分布, 在CUB-200-2011测试数据集上对比异构局部交互图模块对判别性的影响. (a) 不带异构局部交互图模块训练的部件流主干网络输出特征的可视化结果. (b) 学习异构局部交互图模块反馈的信息后部件流网络输出特征的可视化结果
Fig. 6 The clustering distribution of the output features of the part stream backbone network is visualized through t-SNE, comparing the impact of HLIGM on discriminative performance on the CUB-200-2011 test dataset. (a) Visualization results of the output features of the part stream backbone network without the heterogeneous local interaction graph module. (b) Visualization results of the output features of the part stream network after learning feedback information from the HLIGM
表 1 在CUB-200–2011数据集上的对比实验结果, Backbone表示所使用的作为特征提取器的主干网络, Anno./DATA表示是否使用了额外的标注信息或者辅助数据
Table 1 The comparative experimental results on CUB-200–2011 dataset, Backbone indicates the backbone used as a feature extractor, and Anno./DATA indicates whether additional labeling information or auxiliary data is used
Methods Backbone Anno./DATA Accuracy (%) RA-CNN[6] VGG-19 — 85.3 HSnet[29] Inception Anno. 87.5 PART[27] ResNet-50 — 89.6 Mask-CNN[30] VGG-16 Anno. 87.3 S3N[28] ResNet-50 — 88.5 NTSN[46] ResNet-50 — 87.5 ACNet[47] ResNet-50 — 88.1 GDSMP-Net[48] ResNet-101 — 88.1 MetaFGNet[31] ResNet-50 Data 87.6 DCL[37] ResNet-50 — 88.6 DBT[32] ResNet-101 — 88.1 GCL[12] ResNet-50 — 88.3 AENet[19] ResNet-101 — 88.6 MGE-CNN[17] ResNet-101 — 89.4 GHRD[20] ResNet-50 — 89.6 PMG[33] ResNet-50 — 89.9 Ours textNet-50 — 90.2 Ours ResNet-101 — 90.5 
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表 2 在NA Birds数据集上的对比实验结果
Table 2 Comparison Results on NA Birds dataset
Methods Backbone Anno./DATA Accuracy (%) DSTL[49] Inception-v3 — 87.9 MaxEnt[50] DenseNet-161 — 83.0 PMG[33] ResNet-50 — 87.9 MGE-CNN[17] ResNet-101 — 88.6 CS-Part[51] ResNet-50 — 88.5 API-NET[52] ResNet-101 — 88.1 FixSENet-154[35] SENet-154 — 89.2 GHRD[20] ResNet-50 — 88.0 Ours ResNet-50 — 89.5 Ours ResNet-101 — 89.9
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表 3 在StanfordCars数据集上的对比实验结果
Table 3 Comparison Results on StanfordCars dataset
Methods Backbone Anno./DATA Accuracy (%) RA-CNN[6] VGG-19 — 92.5 PSA-CNN[53] VGG-19 Anno. 92.6 HSnet[29] Inception Anno. 93.9 ACNet[47] ResNet-50 — 94.6 S3N[28] ResNet-50 — 94.7 NTSN[46] ResNet-50 — 93.9 DCL[37] ResNet-50 — 94.5 GCL[12] ResNet-50 — 94.0 AENet[19] ResNet-101 — 93.7 MGE-CNN[17] ResNet-101 — 93.9 API-NET[52] ResNet-101 — 94.9 SDNs[54] ResNet-101 — 94.6 M2B[55] ResNet-50 — 94.7 TransFG[36] ViT-B 16 — 94.8 Ours ResNet-50 — 95.1 Ours ResNet-101 — 95.5
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表 4 在FGVC-Aircraft数据集上的对比实验结果
Table 4 Comparison Results on FGVC-Aircraft dataset
Methods Backbone Anno./DATA Accuracy (%) DTRG[56] ResNet-50 — 94.1 MG-CNN[40] VGG-19 Anno. 83.0 ACNet[47] ResNet-50 — 92.4 S3N[28] ResNet-50 — 92.8 NTSN[46] ResNet-50 — 91.4 DCL[37] ResNet-50 — 93.0 DBT[32] ResNet-101 — 91.6 GCL[12] ResNet-50 — 93.2 AENet[19] ResNet-101 — 93.8 API-NET[52] ResNet-101 — 93.4 GHRD[20] ResNet-50 — 94.3 M2B[55] ResNet-50 — 93.3 PMG[33] ResNet-50 — 94.1 Ours ResNet-50 — 94.6 Ours ResNet-101 — 94.8
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表 5 在CUB-200-2011数据集上的消融实验结果
Table 5 Ablation Results on CUB-200-2011 dataset
Methods Accuracy (%) BL 84.5 BL+DP 85.0 BL+DP+HGAIM 88.4 BL+DP+AAOLM 89.3 BL+DP+AAOLM+HGAIM 90.2
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