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

Although existing multi-object tracking (MOT) algorithms have obtainedcompetitive performance on various benchmarks, almost all of them train andvalidate models on the same domain. The domain generalization problem of MOT ishardly studied. To bridge this gap, we first draw the observation that thehigh-level information contained in natural language is domain invariant todifferent tracking domains. Based on this observation, we propose to introducenatural language representation into visual MOT models for boosting the domaingeneralization ability. However, it is infeasible to label every trackingtarget with a textual description. To tackle this problem, we design twomodules, namely visual context prompting (VCP) and visual-language mixing(VLM). Specifically, VCP generates visual prompts based on the input frames.VLM joints the information in the generated visual prompts and the textualprompts from a pre-defined Trackbook to obtain instance-level pseudo textualdescription, which is domain invariant to different tracking scenes. Throughtraining models on MOT17 and validating them on MOT20, we observe that thepseudo textual descriptions generated by our proposed modules improve thegeneralization performance of query-based trackers by large margins.

Generalizing Multiple Object Tracking to Unseen Domains by Introducing Natural Language Representation