MOSAIC: Maximizing out-of-distribution sensitivity via aligned image classification

Out-of-Distribution (OOD) classification is a domain generalization task in computer vision. Deep learning models are typically developed and tested under the implicit assumption that training and test data are drawn independently and identically distributed (IID) from the same distribution. Overlooking OOD images can lead to poor performance under unseen or adverse viewing conditions, which are common in real-world scenarios. In this work, the proposed solution can be described as a data-driven approach to solve the OOD classification task in computer vision. The proposed approach consists of three stages, a training stage for exploiting labeled source data with different data augmentation strategies using powerful pretrained vision transformer models, an intermediate stage for weighted model ensemble and post-processing strategies, and finally an inference stage for exploiting unlabeled target data by using test-time learning. The proposed data-driven approach enhances the OOD generalization ability of deep models that withstand shifts in nuisances such as shape, pose, context, texture, occlusion, and weather in OOD or rare scenarios. Extensive data-augmentation strategies are used to improve the OOD generalization of deep models across various nuisances. The effectiveness of the proposed approach is evaluated using two standard computer vision benchmarks: ROBIN and a test set provided by the OOD-CV Challenge 2023. The experimental results show that the proposed approach demonstrates a performance improvement of 2.73% the ROBIN test set and achieves accuracy of 94.04% for the Challenge test set in terms of OOD robustness evaluation with classification accuracy. Furthermore, the proposed solution has secured a position within the top three OOD-based rankings on the OOD-CV Challenge Image Classification Leaderboard, 2023.