Image attribution algorithms aim to identify important regions that are highly relevant to model decisions. Although existing attribution solutions can effectively assign importance to target elements, they still face the following challenges: 1) existing attribution methods generate inaccurate small regions thus misleading the direction of correct attribution, and 2) the model cannot produce good attribution results for samples with wrong predictions. To address the above challenges, this paper re-models the above image attribution problem as a submodular subset selection problem, aiming to enhance model interpretability using fewer regions. To address the lack of attention to local regions, we construct a novel submodular function to discover more accurate fine-grained interpretation regions. To enhance the attribution effect for all samples, we also impose four different constraints on the selection of sub-regions, i.e., confidence, effectiveness, consistency, and collaboration scores, to assess the importance of various subsets. Moreover, our theoretical analysis substantiates that the proposed function is in fact submodular. Extensive experiments show that the proposed method outperforms SOTA methods on two face datasets (Celeb-A and VGG-Face2) and one fine-grained dataset (CUB-200-2011). For correctly predicted samples, the proposed method improves the Deletion and Insertion scores with an average of 4.9% and 2.5% gain relative to HSIC-Attribution. For incorrectly predicted samples, our method achieves gains of 81.0% and 18.4% compared to the HSIC-Attribution algorithm in the average highest confidence and Insertion score respectively. The code is released at https://github.com/RuoyuChen10/SMDL-Attribution.

翻译：图像归因算法旨在识别与模型决策高度相关的重要区域。尽管现有归因方案能够有效分配重要度给目标元素，但仍面临以下挑战：1) 现有归因方法生成不精确的小区域，从而误导正确归因方向；2) 模型无法对错误预测样本产生良好的归因结果。针对上述挑战，本文将图像归因问题重新建模为子模子集选择问题，旨在使用更少区域增强模型可解释性。为解决对局部区域关注不足，我们构建了一个新型子模函数以发现更精确的细粒度解释区域。为提升所有样本的归因效果，我们同时对子区域选择施加四个不同约束，即置信度、有效性、一致性和协作性评分，以评估不同子集的重要性。此外，理论分析证实所提函数本质上是子模的。大量实验表明，所提方法在两个面部数据集（Celeb-A和VGG-Face2）及一个细粒度数据集（CUB-200-2011）上均优于现有最优方法。对于正确预测样本，所提方法相较于HSIC-Attribution在删除和插入分数上分别平均提升4.9%和2.5%。对于错误预测样本，相较于HSIC-Attribution算法，所提方法在平均最高置信度和插入分数上分别获得81.0%和18.4%的提升。代码已开源至https://github.com/RuoyuChen10/SMDL-Attribution。