In this study, we introduce an intelligent Test Time Augmentation (TTA)
algorithm designed to enhance the robustness and accuracy of image
classification models against viewpoint variations. Unlike traditional TTA
methods that indiscriminately apply augmentations, our approach intelligently
selects optimal augmentations based on predictive uncertainty metrics. This
selection is achieved via a two-stage process: the first stage identifies the
optimal augmentation for each class by evaluating uncertainty levels, while the
second stage implements an uncertainty threshold to determine when applying TTA
would be advantageous. This methodological advancement ensures that
augmentations contribute to classification more effectively than a uniform
application across the dataset. Experimental validation across several datasets
and neural network architectures validates our approach, yielding an average
accuracy improvement of 1.73% over methods that use single-view images. This
research underscores the potential of adaptive, uncertainty-aware TTA in
improving the robustness of image classification in the presence of viewpoint
variations, paving the way for further exploration into intelligent
augmentation strategies.

通过智能测试时间增强 (TTA) 算法，在视角变化的情况下提高图像分类模型的稳健性和准确性，该方法根据预测不确定性指标智能选择最佳增强方法，并通过两个阶段的过程来实现该选择，实验验证证明这种方法可以在多个数据集和神经网络架构上获得平均准确率提高 1.73%，突出了在智能增强策略方面进一步探索的潜力。