  Recently, out-of-distribution (OOD) generalization has attracted attention to
the robustness and generalization ability of deep learning based models, and
accordingly, many strategies have been made to address different aspects
related to this issue. However, most existing algorithms for OOD generalization
are complicated and specifically designed for certain dataset. To alleviate
this problem, nicochallenge-2022 provides NICO++, a large-scale dataset with
diverse context information. In this paper, based on systematic analysis of
different schemes on NICO++ dataset, we propose a simple but effective learning
framework via coupling bag of tricks, including multi-objective framework
design, data augmentations, training and inference strategies. Our algorithm is
memory-efficient and easily-equipped, without complicated modules and does not
require for large pre-trained models. It achieves an excellent performance with
Top-1 accuracy of 88.16% on public test set and 75.65% on private test set, and
ranks 1st in domain generalization task of nicochallenge-2022.
