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

arXiv: https://arxiv.org/abs/2211.09098 · HTML: https://mlsanity.com/paper/2211.09098/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 39 references that are in the map.
- First posted on arXiv 16 Nov 2022.

## Builds on (most cited first, 13 of 13)

- [Self-training with Noisy Student improves ImageNet classification](https://mlsanity.com/paper/1911.04252/) (2019)
- [Bag of Tricks and A Strong Baseline for Deep Person Re-identification](https://mlsanity.com/paper/1903.07071/) (2019)
- [A Large-Scale Car Dataset for Fine-Grained Categorization and Verification](https://mlsanity.com/paper/1506.08959/) (2015)
- [Shield: Fast, Practical Defense and Vaccination for Deep Learning using JPEG Compression](https://mlsanity.com/paper/1802.06816/) (2018)
- [Object Detection with a Unified Label Space from Multiple Datasets](https://mlsanity.com/paper/2008.06614/) (2020)
- [BoxCars: Improving Fine-Grained Recognition of Vehicles using 3-D Bounding Boxes in Traffic Surveillance](https://mlsanity.com/paper/1703.00686/) (2017)
- [Discriminative Adversarial Domain Generalization with Meta-learning based Cross-domain Validation](https://mlsanity.com/paper/2011.00444/) (2020)
- [Looking GLAMORous: Vehicle Re-Id in Heterogeneous Cameras Networks with Global and Local Attention](https://mlsanity.com/paper/2002.02256/) (2020)
- [Vehicle Re-Identification in Context](https://mlsanity.com/paper/1809.09409/) (2018)
- [ODIN: Automated Drift Detection and Recovery in Video Analytics](https://mlsanity.com/paper/2009.05440/) (2020)
- [Cross Domain Knowledge Learning with Dual-branch Adversarial Network for Vehicle Re-identification](https://mlsanity.com/paper/1905.00006/) (2019)
- [Challenges and Opportunities in Rapid Epidemic Information Propagation with Live Knowledge Aggregation from Social Media](https://mlsanity.com/paper/2011.05416/) (2020)
- [Unsupervised Domain Adaptive Person Re-id with Local-enhance and Prototype Dictionary Learning](https://mlsanity.com/paper/2201.03803/) (2022)

## Abstract (arXiv)

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.