ATEAM: Knowledge Integration from Federated Datasets for Vehicle Feature Extraction using Annotation Team of Experts

TL;DR

This paper introduces ATEAM, an expert team-based approach for integrating disjoint vehicle datasets, creating a comprehensive dataset that improves vehicle recognition accuracy without altering existing models.

Contribution

The paper presents a novel annotation team-of-experts method for cross-dataset knowledge transfer and integration in vehicle recognition tasks, resulting in the creation of the Knowledge Integrated Dataset (KID).

Findings

Achieved 0.83 mAP on VeRi dataset.

Achieved 0.97 accuracy on CompCars.

Enabled off-the-shelf models to perform well across integrated datasets.

Abstract

The vehicle recognition area, including vehicle make-model recognition (VMMR), re-id, tracking, and parts-detection, has made significant progress in recent years, driven by several large-scale datasets for each task. These datasets are often non-overlapping, with different label schemas for each task: VMMR focuses on make and model, while re-id focuses on vehicle ID. It is promising to combine these datasets to take advantage of knowledge across datasets as well as increased training data; however, dataset integration is challenging due to the domain gap problem. This paper proposes ATEAM, an annotation team-of-experts to perform cross-dataset labeling and integration of disjoint annotation schemas. ATEAM uses diverse experts, each trained on datasets that contain an annotation schema, to transfer knowledge to datasets without that annotation. Using ATEAM, we integrated several common vehicle recognition datasets into a Knowledge Integrated Dataset (KID). We evaluate ATEAM and KID for vehicle recognition problems and show that our integrated dataset can help off-the-shelf models achieve excellent accuracy on VMMR and vehicle re-id with no changes to model architectures. We achieve mAP of 0.83 on VeRi, and accuracy of 0.97 on CompCars. We have released both the dataset and the ATEAM framework for public use.