  Deep neural networks are likely to fail when the test data is corrupted in
real-world deployment (e.g., blur, weather, etc.). Test-time optimization is an
effective way that adapts models to generalize to corrupted data during
testing, which has been shown in the image domain. However, the techniques for
improving video classification corruption robustness remain few. In this work,
we propose a Temporal Coherent Test-time Optimization framework (TeCo) to
utilize spatio-temporal information in test-time optimization for robust video
classification. To exploit information in video with self-supervised learning,
TeCo uses global content from video clips and optimizes models for entropy
minimization. TeCo minimizes the entropy of the prediction based on the global
content from video clips. Meanwhile, it also feeds local content to regularize
the temporal coherence at the feature level. TeCo retains the generalization
ability of various video classification models and achieves significant
improvements in corruption robustness across Mini Kinetics-C and Mini SSV2-C.
Furthermore, TeCo sets a new baseline in video classification corruption
robustness via test-time optimization.
