Abstract
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.
Abstract (translated)
深度学习网络在真实世界部署中测试数据有损坏时可能会失败(例如,模糊、天气等)。测试时优化是一种有效的方法,可以适应模型在测试期间对损坏数据进行泛化的方法,这在图像领域已经得到了证明。然而,改善视频分类的损坏鲁棒性的方法仍然很少。在本文中,我们提出了一个时间一致性测试时优化框架(TeCo),利用时间信息在测试时优化视频分类的鲁棒性。为了利用自监督学习视频中的信息,TeCo使用视频片段中的全局内容并优化模型的熵最小化。TeCo基于视频片段中的全局内容最小化预测熵。同时,它还在特征级别上 feed 当地内容,以 regularize 时间一致性。TeCo保留了各种视频分类模型的泛化能力,并在 Mini Kinetics-C 和 Mini SSV2-C 之间实现了显著的损坏鲁棒性改善。此外,通过测试时优化,TeCo 在视频分类的损坏鲁棒性方面也提供了一个新的基础。
URL
https://arxiv.org/abs/2302.14309
