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

Natural language (NL) based vehicle retrieval is a task aiming to retrieve avehicle that is most consistent with a given NL query from among all candidatevehicles. Because NL query can be easily obtained, such a task has a promisingprospect in building an interactive intelligent traffic system (ITS). Currentsolutions mainly focus on extracting both text and image features and mappingthem to the same latent space to compare the similarity. However, existingmethods usually use dependency analysis or semantic role-labelling techniquesto find keywords related to vehicle attributes. These techniques may require alot of pre-processing and post-processing work, and also suffer from extractingthe wrong keyword when the NL query is complex. To tackle these problems andsimplify, we borrow the idea from named entity recognition (NER) and constructFindVehicle, a NER dataset in the traffic domain. It has 42.3k labelled NLdescriptions of vehicle tracks, containing information such as the location,orientation, type and colour of the vehicle. FindVehicle also adopts bothoverlapping entities and fine-grained entities to meet further requirements. Toverify its effectiveness, we propose a baseline NL-based vehicle retrievalmodel called VehicleFinder. Our experiment shows that by using text encoderspre-trained by FindVehicle, VehicleFinder achieves 87.7\% precision and 89.4\%recall when retrieving a target vehicle by text command on our homemade datasetbased on UA-DETRAC. The time cost of VehicleFinder is 279.35 ms on one ARM v8.2CPU and 93.72 ms on one RTX A4000 GPU, which is much faster than theTransformer-based system. The dataset is open-source via the linkhttps://github.com/GuanRunwei/FindVehicle, and the implementation can be foundvia the link https://github.com/GuanRunwei/VehicleFinder-CTIM.

FindVehicle and VehicleFinder: A NER dataset for natural language-based vehicle retrieval and a keyword-based cross-modal vehicle retrieval system