Unsupervised transfer of object recognition models from synthetic to real
data is an important problem with many potential applications. The challenge is
how to "adapt" a model trained on simulated images so that it performs well on
real-world data without any additional supervision. Unfortunately, current
benchmarks for this problem are limited in size and task diversity. In this
paper, we present a new large-scale benchmark called Syn2Real, which consists
of a synthetic domain rendered from 3D object models and two real-image domains
containing the same object categories. We define three related tasks on this
benchmark: closed-set object classification, open-set object classification,
and object detection. Our evaluation of multiple state-of-the-art methods
reveals a large gap in adaptation performance between the easier closed-set
classification task and the more difficult open-set and detection tasks. We
conclude that developing adaptation methods that work well across all three
tasks presents a significant future challenge for syn2real domain transfer.

本文介绍了一个新的大型基准测试 Syn2Real，其中包括了一个从 3D 物体模型渲染出的合成域以及两个包含相同物体类别的真实图像域，分别定义了三个相关任务：封闭集物体分类、开放集物体分类和物体检测。评估多种最先进的方法揭示了现有方法在不同任务上存在的许多适应性问题。