Simulating Content Consistent Vehicle Datasets with Attribute Descent
This work addresses domain adaptation for vehicle re-identification, an incremental improvement focusing on content-level gaps orthogonal to appearance methods.
The paper tackles the content gap between synthetic and real vehicle data by creating a large-scale synthetic dataset (VehicleX) and using an attribute descent approach to align attributes like illumination and viewpoint with real data, achieving competitive accuracy in vehicle re-identification when mixed with real datasets.
This paper uses a graphic engine to simulate a large amount of training data with free annotations. Between synthetic and real data, there is a two-level domain gap, i.e., content level and appearance level. While the latter has been widely studied, we focus on reducing the content gap in attributes like illumination and viewpoint. To reduce the problem complexity, we choose a smaller and more controllable application, vehicle re-identification (re-ID). We introduce a large-scale synthetic dataset VehicleX. Created in Unity, it contains 1,362 vehicles of various 3D models with fully editable attributes. We propose an attribute descent approach to let VehicleX approximate the attributes in real-world datasets. Specifically, we manipulate each attribute in VehicleX, aiming to minimize the discrepancy between VehicleX and real data in terms of the Fréchet Inception Distance (FID). This attribute descent algorithm allows content domain adaptation (DA) orthogonal to existing appearance DA methods. We mix the optimized VehicleX data with real-world vehicle re-ID datasets, and observe consistent improvement. With the augmented datasets, we report competitive accuracy. We make the dataset, engine and our codes available at https://github.com/yorkeyao/VehicleX.