Attribute Descent: Simulating Object-Centric Datasets on the Content Level and Beyond

• URL: http://arxiv.org/abs/2202.14034v2
• Date: Wed, 29 Nov 2023 23:39:40 GMT
• Title: Attribute Descent: Simulating Object-Centric Datasets on the Content Level and Beyond
• Authors: Yue Yao, Liang Zheng, Xiaodong Yang, Milind Napthade, and Tom Gedeon
• Abstract summary: Between synthetic and real, a two-level domain gap exists, involving content level and appearance level. We propose an attribute descent approach that automatically optimize engine attributes to enable synthetic data to approximate real-world data. Experiments on image classification and object re-identification confirm that adapted synthetic data can be effectively used in three scenarios.
• Score: 17.949962340691673
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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