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 Tools annual

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 Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Tracking by natural language specification aims to locate the referred targetin a sequence based on the natural language description. Existing algorithmssolve this issue in two steps, visual grounding and tracking, and accordinglydeploy the separated grounding model and tracking model to implement these twosteps, respectively. Such a separated framework overlooks the link betweenvisual grounding and tracking, which is that the natural language descriptionsprovide global semantic cues for localizing the target for both two steps.Besides, the separated framework can hardly be trained end-to-end. To handlethese issues, we propose a joint visual grounding and tracking framework, whichreformulates grounding and tracking as a unified task: localizing the referredtarget based on the given visual-language references. Specifically, we proposea multi-source relation modeling module to effectively build the relationbetween the visual-language references and the test image. In addition, wedesign a temporal modeling module to provide a temporal clue with the guidanceof the global semantic information for our model, which effectively improvesthe adaptability to the appearance variations of the target. Extensiveexperimental results on TNL2K, LaSOT, OTB99, and RefCOCOg demonstrate that ourmethod performs favorably against state-of-the-art algorithms for both trackingand grounding. Code is available at https://github.com/lizhou-cs/JointNLT.

Joint Visual Grounding and Tracking with Natural Language Specification