ATEAM: Knowledge Integration from Federated Datasets for Vehicle Feature
Extraction using Annotation Team of Experts
- URL: http://arxiv.org/abs/2211.09098v1
- Date: Wed, 16 Nov 2022 18:33:45 GMT
- Title: ATEAM: Knowledge Integration from Federated Datasets for Vehicle Feature
Extraction using Annotation Team of Experts
- Authors: Abhijit Suprem, Purva Singh, Suma Cherkadi, Sanjyot Vaidya, Joao
Eduardo Ferreira, and Calton Pu
- Abstract summary: This paper proposes ATEAM, an annotation team-of-experts, to perform cross-dataset labeling and integration of disjoint annotation schemas.
We 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.
- Score: 1.947162363730401
- License: http://creativecommons.org/licenses/by/4.0/
- 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.
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