局部描述符学习的自监督训练样本难度平衡
Self-supervised Training Sample Difficulty Balancing for Local Descriptor Learning
摘要：
在正样本和负样本之间不平衡的情况下，硬负挖掘策略已被证明可以帮助模型学习正样本和负样本之间更细微的差异，从而提高识别性能。但是，如果在数据集中推广过于严格的挖掘策略，则可能存在引入假阴性样本的风险。同时，挖掘策略的实施破坏了真实数据集中样本的难度分布，这可能导致模型过拟合这些困难的样本。因此，本文研究了如何权衡样本挖掘的难度，以获取和开发高质量的负样本，并试图从损失函数和训练策略两个方面解决这一问题。平衡损失通过将自监督方法与损失函数相结合，为阴性样本的质量提供了有效的判别，并使用动态梯度调制策略来实现对不同难度的样本的更精细的梯度调整。然后，提出的退火训练策略约束从负样本挖掘中提取的样本的难度，为损失函数提供具有不同难度分布的数据源，并使用降低难度的样本来训练模型。大量的实验表明，我们的新描述符优于以前的国家的最先进的描述符补丁验证，匹配和检索任务。
英文摘要：
In the case of an imbalance between positive and negative samples, hard negative mining strategies have been shown to help models learn more subtle differences between positive and negative samples, thus improving recognition performance. However, if too strict mining strategies are promoted in the dataset, there may be a risk of introducing false negative samples. Meanwhile, the implementation of the mining strategy disrupts the difficulty distribution of samples in the real dataset, which may cause the model to over-fit these difficult samples. Therefore, in this paper, we investigate how to trade off the difficulty of the mined samples in order to obtain and exploit high-quality negative samples, and try to solve the problem in terms of both the loss function and the training strategy. The proposed balance loss provides an effective discriminant for the quality of negative samples by combining a self-supervised approach to the loss function, and uses a dynamic gradient modulation strategy to achieve finer gradient adjustment for samples of different difficulties. The proposed annealing training strategy then constrains the difficulty of the samples drawn from negative sample mining to provide data sources with different difficulty distributions for the loss function, and uses samples of decreasing difficulty to train the model. Extensive experiments show that our new descriptors outperform previous state-of-the-art descriptors for patch validation, matching, and retrieval tasks.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
