Abstract:Deep neural networks are likely to fail when the test data is corrupted in realworld 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 spatiotemporal information in test-time optimizat… Show more
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