Simulating Content Consistent Vehicle Datasets with Attribute Descent

TL;DR

This paper introduces VehicleX, a large-scale synthetic vehicle dataset created with a graphic engine, and proposes an attribute descent method to reduce content domain gap in vehicle re-identification tasks, improving accuracy.

Contribution

The paper presents a novel attribute descent approach for content domain adaptation in synthetic vehicle datasets, enhancing vehicle re-ID performance.

Findings

Mixing VehicleX with real data improves re-ID accuracy.

Attribute descent reduces content gap in attributes like illumination and viewpoint.

VehicleX dataset and code are publicly available.

Abstract

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 Fr\'echet 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.