Self-supervised Training Sample Difficulty Balancing for Local Descriptor Learning

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.