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摘要: 基于图像级标签的弱监督语义分割算法因极低的标注成本引起学界广泛关注. 该领域的算法利用分类网络产生的类激活图实现从图像级标签到像素级标签的转化. 然而类激活图往往只关注于图像中最显著的区域, 致使基于类激活图产生的伪标签与真实标注存在较大差距, 主要包括前景未被有效激活的欠激活问题以及前景间预测混淆的错误激活问题. 欠激活源于数据集类内差异过大, 致使单一分类器不足以准确识别同一类别的所有像素, 错误激活则是数据集类间差异过小, 导致分类器不能有效区分不同类别的像素. 本文考虑到同一类别像素在图像内的差异小于在数据集中的差异, 设计基于类中心的图像特定分类器, 以提升对同类像素的识别能力, 从而改善欠激活, 同时考虑到类中心是类别在特征空间的代表, 设计类中心约束函数, 通过扩大类中心间的差距从而间接地疏远不同类别的特征分布, 以缓解错误激活现象. 图像特定分类器可以插入其他弱监督语义分割网络, 替代分类网络的分类器, 以产生更高质量的类激活图. 实验结果表明, 本文所提出的方案在两个基准数据集上均具有良好的表现, 证实该方案的有效性.Abstract: Weakly supervised semantic segmentation algorithms based on image-level labels have garnered widespread attention due to their low annotation costs. These algorithms utilize class activation maps (CAMs) generated by classification networks to convert from image-level labels to pixel-level labels. However, CAMs only focus on the most discriminative regions in the image, resulting in a large gap between pseudo-labels generated by CAMs and ground truth. The gap includes under-activation and mis-activation issues. Under-activation arises from excessive intra-class difference in the dataset, making a single classifier insufficient to accurately identify all pixels of the same category, while mis-activation occurs when inter-class differences are too small, preventing the classifier from effectively distinguishing pixels of different categories. This paper considers that the intra-class difference for pixels of the same class within an image is smaller than that of the dataset, so we design the image-specific classifier based on the class center to enhance the recognition capability for pixels of the same class, thereby addressing the under-activation issue. Besides, considering that the class center serves as the representative of class in the feature space, we design the class center constrained loss function. By enlarging the distances of different class centers, this function indirectly pushes apart the feature distributions of different classes, thereby mitigating the issue of mis-activation. The proposed image-specific classifier can be plugged into other weakly supervised semantic segmentation networks, which can replace the classifiers of classification networks to generate higher quality CAMs. Experimental results demonstrate superior performance of the proposed methods on two benchmark datasets, validating the effectiveness of this method.
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图 1 类激活图与特征、全局分类器以及图像特定分类器在t-SNE下的可视化结果
Fig. 1 The visualization results of CAMs, features, global classifier, and ISC under t-SNE
图 4 不同的$ \tau $对模型性能的影响
Fig. 4 The impact of different $ \tau $ values on the model＇s performance
图 5 不同方法产生的伪标签的可视化
Fig. 5 The visualization of the pseudo mask under different methods
图 6 DeepLabV2在PASCAL VOC 2012验证集上的预测结果与真实标签对比
Fig. 6 Comparison of the segmentation results with the ground truth on the PASCAL VOC 2012 validation set
表 1 本方案中各组件性能对比
Table 1 Comparison of the performance about each component in this approach
R-50 ISC $ L_{cccl} $ 监督 优化 mIoU (%) CRF IRN CRF IRN $ \checkmark$ 48.5 $ \checkmark$ $ \checkmark$ 52.4 $ \checkmark$ $ \checkmark$ 66.5 $ \checkmark$ $ \checkmark$ $ \checkmark$ 54.3 $ \checkmark$ $ \checkmark$ $ \checkmark$ $ \checkmark$ 54.7 $ \checkmark$ $ \checkmark$ $ \checkmark$ 59.5 $ \checkmark$ $ \checkmark$ $ \checkmark$ $ \checkmark$ 61.5 $ \checkmark$ $ \checkmark$ $ \checkmark$ $ \checkmark$ 68.7 $ \checkmark$ $ \checkmark$ $ \checkmark$ $ \checkmark$ $ \checkmark$ 69.5 
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表 2 不同相似度度量算法对比
Table 2 Comparison of different similarity measurement algorithms
方法 B-Net 距离 角度 卷积 mIoU (%) ResNet50 51.3 50.5 54.3
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表 3 基于本文方法产生的类激活图以及伪标签与其他方法在PASCAL VOC 2012数据集上的对比 ($ \dagger $和$ * $分别表示骨架网络基于ImageNet21K预训练以及使用了显著性图, Ours表示同时使用ISC和$ L_{cccl} $)
Table 3 Comparison about CAMs and pseudo labels generated by our method with those from other methods on the PASCAL VOC 2012 dataset ($ \dagger $ and $ * $ denote that the backbone is pretrained on ImageNet21K and uses saliency map, respectively, and Ours denotes the simultaneous use of ISC and $ L_{cccl} $)
方法 CAM (%) 伪标签(%) SEAM[15] 55.4 63.6 IRN[23] 48.3 66.5 CLIPES[41] 70.8 75.0 CLIPES+CPAL[42] 71.9 75.8 AdvCAM[43] 55.6 68.9 MCTformer[26] 61.7 69.1 FPR[44] 63.8 68.5 SEAM+OCR[45] 67.8 68.4 EPS*[24] 69.5 71.6 VIT-PCM†[46] 67.7 71.4 ToCo†[27] — 73.6 EPS*+PPC[14] 70.5 73.3 KTSE[47] 67.0 73.8 IRN+Ours 61.5 69.5 SEAM+Ours 65.5 70.7 EPS*+Ours 71.9 74.4 CLIPES+SEAM+Ours 69.6 76.1 CLIPES+EPS*+Ours 73.5 76.8
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表 4 与其他先进方法在PASCAL VOC 2012数据集上的分割性能对比 ($ I $, $ S $和$ L $分别表示图像级标签、显著性图和大语言模型)
Table 4 Comparison of the segmentation performance with the state-of-the-art methods on the PASCAL VOC 2012 dataset ($ I $, $ S $ and $ L $ denote image-level label, saliency map and large language model, respectively)
方法 监督 验证集(%) 测试集(%) IRN[23] I 63.5 64.8 SEAM[15] I 64.5 65.7 SEAM+OCR[45] I 67.8 68.4 IRN+ReCAM[37] I 68.7 68.5 IRN+LPCAM[20] I 68.6 68.7 CLIMS[19] I+L 70.4 70.0 CLIPES[41] I+L 71.1 71.4 MCTformer[26] I 71.9 71.6 KTSE[47] I 73.0 72.9 CTI[48] I 74.1 73.8 CLIPES+CPAL[42] I+L 74.5 74.7 AuxSegNet[49] I+S 69.0 68.6 Yazhou Yao[50] I+S 70.4 70.2 EPS[24] I+S 70.9 70.8 SANCE[51] I+S 72.0 72.9 RCA[52] I+S 72.2 72.8 EPS+PPC[14] I+S 72.6 73.6 SEAM+Ours I 68.9 70.0 IRN+Ours I 69.5 70.0 EPS+Ours I+S 73.1 73.5 CLIPES+SEAM+Ours I+L 74.1 74.1 CLIPES+EPS+Ours I+S+L 74.4 74.8
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表 5 与其他先进方法在MS COCO 2014数据集上的分割性能对比
Table 5 Comparison of the segmentation performance with the state-of-the-art methods on the MS COCO 2014 dataset
方法 骨架网络 监督 验证集(%) PSA[9] ResNet38 I 29.5 SEAM[15] ResNet38 I 31.9 SEAM+OCR[45] ResNet38 I 33.2 CDA[53] ResNet38 I 33.2 URN[31] ResNet101 I 40.7 IRN[23] ResNet101 I 42.0 IRN+ReCAM[37] ResNet101 I 42.9 IRN+LPCAM[20] ResNet101 I 44.5 RIB[54] ResNet101 I 44.5 BECO[32] ResNet101 I 45.1 EPS[24] ResNet101 I+S 35.7 RCA[52] ResNet101 I+S 36.8 L2G[55] ResNet101 I+S 44.2 IRN+Ours ResNet101 I 44.6 BECO+Ours ResNet101 I 45.4
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表 6 与其他可插入性方法的计算开销对比
Table 6 Comparison of computational overhead with other pluggable methods
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表 7 不同形式的类中心约束函数对实验结果的影响, 其中基线代表ResNet50+ISC
Table 7 The impact of different forms of class center constrained loss function on experimental results, where the baseline represents ResNet50+ISC
方法 基线 $ L_{cccl} $ $ L_{info} $ $ {\boldsymbol{w}} $ $ {{\boldsymbol{w}}_{{\boldsymbol{aux}}}} $ mIoU (%) 59.5 61.5 61.0 60.4
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