论文标题
与双层嘈杂对应关系的图形匹配
Graph Matching with Bi-level Noisy Correspondence
论文作者
论文摘要
在本文中，我们研究了图形匹配（GM）中的一个新颖且广泛存在的问题，即双级噪声对应（BNC），它指的是节点级噪声对应（NNC）和边缘级噪声通讯（ENC）。简而言之，一方面，由于图像之间的可识别性和观点差异的差异，不可避免地注释一些具有偏移和混乱的关键点，从而导致两个相关节点之间的不匹配，即NNC。另一方面，嘈杂的节点到节点的对应关系将进一步污染边缘到边缘的对应关系，从而导致ENC。对于BNC挑战，我们提出了一种新型方法，称为对比度与动量蒸馏的匹配。具体而言，所提出的方法具有强大的二次对比损失，具有以下优点：i）通过通用汽车定制的二次对比度学习范式更好地探索节点对节点和边缘到边缘相关性； ii）根据动量老师估计的信心，适应惩罚嘈杂的任务。与12个竞争基线相比，在三个现实世界数据集上进行了广泛的实验表明了我们模型的鲁棒性。该代码可在https://github.com/xlearning-scu/2023-iccv-common上找到。
In this paper, we study a novel and widely existing problem in graph matching (GM), namely, Bi-level Noisy Correspondence (BNC), which refers to node-level noisy correspondence (NNC) and edge-level noisy correspondence (ENC). In brief, on the one hand, due to the poor recognizability and viewpoint differences between images, it is inevitable to inaccurately annotate some keypoints with offset and confusion, leading to the mismatch between two associated nodes, i.e., NNC. On the other hand, the noisy node-to-node correspondence will further contaminate the edge-to-edge correspondence, thus leading to ENC. For the BNC challenge, we propose a novel method termed Contrastive Matching with Momentum Distillation. Specifically, the proposed method is with a robust quadratic contrastive loss which enjoys the following merits: i) better exploring the node-to-node and edge-to-edge correlations through a GM customized quadratic contrastive learning paradigm; ii) adaptively penalizing the noisy assignments based on the confidence estimated by the momentum teacher. Extensive experiments on three real-world datasets show the robustness of our model compared with 12 competitive baselines. The code is available at https://github.com/XLearning-SCU/2023-ICCV-COMMON.
