English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

The vehicle recognition area, including vehicle make-model recognition(VMMR), re-id, tracking, and parts-detection, has made significant progress inrecent years, driven by several large-scale datasets for each task. Thesedatasets 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 ispromising to combine these datasets to take advantage of knowledge acrossdatasets as well as increased training data; however, dataset integration ischallenging due to the domain gap problem. This paper proposes ATEAM, anannotation team-of-experts to perform cross-dataset labeling and integration ofdisjoint annotation schemas. ATEAM uses diverse experts, each trained ondatasets that contain an annotation schema, to transfer knowledge to datasetswithout that annotation. Using ATEAM, we integrated several common vehiclerecognition datasets into a Knowledge Integrated Dataset (KID). We evaluateATEAM and KID for vehicle recognition problems and show that our integrateddataset can help off-the-shelf models achieve excellent accuracy on VMMR andvehicle re-id with no changes to model architectures. We achieve mAP of 0.83 onVeRi, and accuracy of 0.97 on CompCars. We have released both the dataset andthe ATEAM framework for public use.

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