属性下降：在内容层面及更广范围模拟以对象为中心的数据集
Attribute Descent: Simulating Object-Centric Datasets on the Content Level and Beyond
AI总结：
本文提出属性下降方法，通过自动优化图形引擎属性缩小合成数据与真实数据在内容层面的域差距，并在以对象为中心的分类与重识别任务中验证了其有效性。
摘要：
本文旨在利用图形引擎模拟大量具有免费标注且可能与真实世界数据高度相似的训练数据。在合成数据与真实数据之间，存在一个两级域差距，涉及内容层面和外观层面。后者关注外观风格，而前者问题源于一种不同的机制，即相机视角、物体摆放和光照条件等属性上的内容不匹配。与已被广泛研究的外观层面差距相比，内容层面的差异尚未得到广泛研究。为了解决内容层面的错位问题，我们提出了一种属性下降方法，该方法自动优化引擎属性，使合成数据能够逼近真实世界数据。我们在以对象为中心的任务上验证了我们的方法，在这类任务中，一个对象占据图像的主要部分。在这些任务中，搜索空间相对较小，且每个属性的优化都能产生足够明显的监督信号。我们收集了一个新的合成资产VehicleX，并重新格式化并复用了现有的合成资产ObjectX和PersonX。在图像分类和对象重识别上的大量实验证实，适配后的合成数据可以有效地用于三种场景：仅使用合成数据进行训练、训练数据增强以及从数值上理解数据集内容。
英文摘要：
This article aims to use graphic engines to simulate a large number of training data that have free annotations and possibly strongly resemble to real-world data. Between synthetic and real, a two-level domain gap exists, involving content level and appearance level. While the latter is concerned with appearance style, the former problem arises from a different mechanism, i.e, content mismatch in attributes such as camera viewpoint, object placement and lighting conditions. In contrast to the widely-studied appearance-level gap, the content-level discrepancy has not been broadly studied. To address the content-level misalignment, we propose an attribute descent approach that automatically optimizes engine attributes to enable synthetic data to approximate real-world data. We verify our method on object-centric tasks, wherein an object takes up a major portion of an image. In these tasks, the search space is relatively small, and the optimization of each attribute yields sufficiently obvious supervision signals. We collect a new synthetic asset VehicleX, and reformat and reuse existing the synthetic assets ObjectX and PersonX. Extensive experiments on image classification and object re-identification confirm that adapted synthetic data can be effectively used in three scenarios: training with synthetic data only, training data augmentation and numerically understanding dataset content.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
