Simulating Content Consistent Vehicle Datasets with Attribute Descent (1912.08855v2)

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.

Overview of "Simulating Content Consistent Vehicle Datasets with Attribute Descent"

The paper presents an innovative approach to bridging the domain gap between synthetic and real-world datasets at the content level, specifically focusing on the vehicle re-identification (re-ID) domain. The authors introduce a novel dataset titled VehicleX, simulated using the Unity game engine, and propose the attribute descent method designed to minimize discrepancies in content attributes such as illumination and viewpoint between synthetic and real-world datasets.

Key Contributions

1. VehicleX Dataset: The creation of the VehicleX dataset serves as a crucial foundation for this research. It includes 1,362 synthetic vehicles with editable attributes, modeled in Unity, to allow extensive manipulation of environmental factors that influence re-ID tasks.
2. Attribute Descent Method: The paper introduces an attribute descent algorithm aimed at adjusting attribute values in the VehicleX dataset to minimize the Fréchet Inception Distance (FID) between synthetic and real-world vehicle images. This approach addresses the content level differences, supplementing the often-focused-on appearance level adaptations.
3. Evaluation and Results: Through comprehensive experiments, the authors demonstrate that combining optimized VehicleX data with real-world datasets enhances re-ID accuracy. The optimization of content attributes in synthetic data yields marginal improvements in matching and identifying vehicles across datasets with distinct characteristics.

Numerical Results

The experimental validation of the proposed method includes training models using only the VehicleX dataset and in combination with real-world datasets. Consistently improved accuracy is recorded when synthetic data with optimized attributes is used, showcasing competitive results compared to state-of-the-art methods. Specifically, joint training with VehicleX data exhibits enhanced performance metrics such as Rank-1 accuracy and mean Average Precision (mAP) on various vehicle re-ID datasets - VehicleID, VeRi-776, and CityFlow.

Implications and Future Work

The implications of this research are multifaceted:

• Practical Application in AI Systems: By reducing reliance on extensive and sometimes costly real-world data collection, the methods introduced have practical implications in deploying scalable, flexible AI systems in vehicle re-identification. This approach also mitigates privacy concerns associated with real-world data collection.
• Expanding Domain Adaptation Strategies: The focus on content domain adaptation extends current traditional approaches, providing insights that could inform future enhancements in synthetic data generation for various computer vision tasks.
• Speculations on Future Developments: Insights from this work could lead to further explorations into reducing domain gaps in other re-ID tasks or object recognition challenges, especially those requiring fine-grained distinction adjustments.

This research underscores the potential of simulation environments to produce high-quality, adaptable datasets for training machine learning algorithms, thereby facilitating more robust and generalizable models irrespective of the domain specifics.