论文标题
关于通过数据失真测量闭塞鲁棒性的陷阱
On Pitfalls of Measuring Occlusion Robustness through Data Distortion
论文作者
论文摘要
在过去的几年中，该领域对架构和培训程序的关注在很大程度上受到了数据的关键作用。我们通常会导致数据更改，而不会意识到它们的更广泛含义。在本文中，我们表明，在建立闭塞鲁棒性时，扭曲图像而不考虑引入的人工制品会导致结果偏差。为了确保模型在实际情况下的表现如预期的那样，我们需要排除添加的伪像对评估的影响。我们提出了一种新的方法，即iCclusion，作为未知可能的封闭器的应用程序。
Over the past years, the crucial role of data has largely been shadowed by the field's focus on architectures and training procedures. We often cause changes to the data without being aware of their wider implications. In this paper we show that distorting images without accounting for the artefacts introduced leads to biased results when establishing occlusion robustness. To ensure models behave as expected in real-world scenarios, we need to rule out the impact added artefacts have on evaluation. We propose a new approach, iOcclusion, as a fairer alternative for applications where the possible occluders are unknown.
