论文标题
视频自学学习中的基准敏感性有多严重？
How Severe is Benchmark-Sensitivity in Video Self-Supervised Learning?
论文作者
论文摘要
尽管视频自我监督的学习模型最近取得了成功，但关于它们的概括能力仍然有很多了解。在本文中，我们研究了敏感的视频自我监督学习对当前常规基准的方式以及方法是否超出规范评估设置的概括。我们在敏感性的四个不同因素上这样做：域，样本，动作和任务。我们的研究包括7个视频数据集，9种自学方法和6种视频理解任务的500多个实验，揭示了当前的视频自我监督学习中的基准测试不是沿这些敏感性因素的概括指标。此外，我们发现自我监督的方法在监督前训练的监督前训练后落后，尤其是当域移动较大并且可用下游样品的数量较低时。从我们的分析中，我们将严重的基准测试（实验的一个子集）提炼出来，并讨论其对评估现有和未来自我监督视频学习方法获得的表示的普遍性的含义。
Despite the recent success of video self-supervised learning models, there is much still to be understood about their generalization capability. In this paper, we investigate how sensitive video self-supervised learning is to the current conventional benchmark and whether methods generalize beyond the canonical evaluation setting. We do this across four different factors of sensitivity: domain, samples, actions and task. Our study which encompasses over 500 experiments on 7 video datasets, 9 self-supervised methods and 6 video understanding tasks, reveals that current benchmarks in video self-supervised learning are not good indicators of generalization along these sensitivity factors. Further, we find that self-supervised methods considerably lag behind vanilla supervised pre-training, especially when domain shift is large and the amount of available downstream samples are low. From our analysis, we distill the SEVERE-benchmark, a subset of our experiments, and discuss its implication for evaluating the generalizability of representations obtained by existing and future self-supervised video learning methods.
