论文标题
ObjectNet数据集：重新分析和校正
ObjectNet Dataset: Reanalysis and Correction
论文作者
论文摘要
最近，Barbu等人介绍了一个名为ObjectNet的数据集，该数据集在日常生活情况下包含对象。他们在此数据集上显示了最先进的对象识别模型的性能下降。由于其结果在深层模型的概括能力上的重要性和含义，我们对他们的发现进行了审核。我们强调了他们的工作一个主要问题，该问题将对象识别器应用于包含多个对象而不是孤立对象的场景。后者使用我们的代码导致大约20-30％的性能增长。与ObjectNet论文中报告的结果相比，我们观察到，在没有任何测试时间数据的情况下，可以恢复大约10-15％的性能损失。然而，根据Barbu等人的结论，我们还得出结论，该数据集在此数据集上遭受了巨大影响。因此，我们认为对象网仍然是一个充满挑战的数据集，用于测试模型的概括能力以外的数据集。
Recently, Barbu et al introduced a dataset called ObjectNet which includes objects in daily life situations. They showed a dramatic performance drop of the state of the art object recognition models on this dataset. Due to the importance and implications of their results regarding generalization ability of deep models, we take a second look at their findings. We highlight a major problem with their work which is applying object recognizers to the scenes containing multiple objects rather than isolated objects. The latter results in around 20-30% performance gain using our code. Compared with the results reported in the ObjectNet paper, we observe that around 10-15 % of the performance loss can be recovered, without any test time data augmentation. In accordance with Barbu et al.'s conclusions, however, we also conclude that deep models suffer drastically on this dataset. Thus, we believe that ObjectNet remains a challenging dataset for testing the generalization power of models beyond datasets on which they have been trained.
