Matches in SemOpenAlex for { <https://semopenalex.org/work/W4310823043> ?p ?o ?g. }
Showing items 1 to 82 of
82
with 100 items per page.
- W4310823043 abstract "Although existing multi-object tracking (MOT) algorithms have obtained competitive performance on various benchmarks, almost all of them train and validate models on the same domain. The domain generalization problem of MOT is hardly studied. To bridge this gap, we first draw the observation that the high-level information contained in natural language is domain invariant to different tracking domains. Based on this observation, we propose to introduce natural language representation into visual MOT models for boosting the domain generalization ability. However, it is infeasible to label every tracking target with a textual description. To tackle this problem, we design two modules, 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 textual prompts from a pre-defined Trackbook to obtain instance-level pseudo textual description, which is domain invariant to different tracking scenes. Through training models on MOT17 and validating them on MOT20, we observe that the pseudo textual descriptions generated by our proposed modules improve the generalization performance of query-based trackers by large margins." @default.
- W4310823043 created "2022-12-18" @default.
- W4310823043 creator A5019477857 @default.
- W4310823043 creator A5045154172 @default.
- W4310823043 creator A5047586627 @default.
- W4310823043 creator A5060276608 @default.
- W4310823043 creator A5074521681 @default.
- W4310823043 creator A5081985247 @default.
- W4310823043 creator A5087239641 @default.
- W4310823043 date "2022-12-03" @default.
- W4310823043 modified "2023-10-10" @default.
- W4310823043 title "Generalizing Multiple Object Tracking to Unseen Domains by Introducing Natural Language Representation" @default.
- W4310823043 doi "https://doi.org/10.48550/arxiv.2212.01568" @default.
- W4310823043 hasPublicationYear "2022" @default.
- W4310823043 type Work @default.
- W4310823043 citedByCount "0" @default.
- W4310823043 crossrefType "posted-content" @default.
- W4310823043 hasAuthorship W4310823043A5019477857 @default.
- W4310823043 hasAuthorship W4310823043A5045154172 @default.
- W4310823043 hasAuthorship W4310823043A5047586627 @default.
- W4310823043 hasAuthorship W4310823043A5060276608 @default.
- W4310823043 hasAuthorship W4310823043A5074521681 @default.
- W4310823043 hasAuthorship W4310823043A5081985247 @default.
- W4310823043 hasAuthorship W4310823043A5087239641 @default.
- W4310823043 hasBestOaLocation W43108230431 @default.
- W4310823043 hasConcept C134306372 @default.
- W4310823043 hasConcept C154945302 @default.
- W4310823043 hasConcept C177148314 @default.
- W4310823043 hasConcept C17744445 @default.
- W4310823043 hasConcept C190470478 @default.
- W4310823043 hasConcept C195324797 @default.
- W4310823043 hasConcept C199539241 @default.
- W4310823043 hasConcept C202474056 @default.
- W4310823043 hasConcept C204321447 @default.
- W4310823043 hasConcept C2776359362 @default.
- W4310823043 hasConcept C2781238097 @default.
- W4310823043 hasConcept C31972630 @default.
- W4310823043 hasConcept C33923547 @default.
- W4310823043 hasConcept C36503486 @default.
- W4310823043 hasConcept C37914503 @default.
- W4310823043 hasConcept C41008148 @default.
- W4310823043 hasConcept C46686674 @default.
- W4310823043 hasConcept C56461940 @default.
- W4310823043 hasConcept C57501372 @default.
- W4310823043 hasConcept C94625758 @default.
- W4310823043 hasConceptScore W4310823043C134306372 @default.
- W4310823043 hasConceptScore W4310823043C154945302 @default.
- W4310823043 hasConceptScore W4310823043C177148314 @default.
- W4310823043 hasConceptScore W4310823043C17744445 @default.
- W4310823043 hasConceptScore W4310823043C190470478 @default.
- W4310823043 hasConceptScore W4310823043C195324797 @default.
- W4310823043 hasConceptScore W4310823043C199539241 @default.
- W4310823043 hasConceptScore W4310823043C202474056 @default.
- W4310823043 hasConceptScore W4310823043C204321447 @default.
- W4310823043 hasConceptScore W4310823043C2776359362 @default.
- W4310823043 hasConceptScore W4310823043C2781238097 @default.
- W4310823043 hasConceptScore W4310823043C31972630 @default.
- W4310823043 hasConceptScore W4310823043C33923547 @default.
- W4310823043 hasConceptScore W4310823043C36503486 @default.
- W4310823043 hasConceptScore W4310823043C37914503 @default.
- W4310823043 hasConceptScore W4310823043C41008148 @default.
- W4310823043 hasConceptScore W4310823043C46686674 @default.
- W4310823043 hasConceptScore W4310823043C56461940 @default.
- W4310823043 hasConceptScore W4310823043C57501372 @default.
- W4310823043 hasConceptScore W4310823043C94625758 @default.
- W4310823043 hasLocation W43108230431 @default.
- W4310823043 hasLocation W43108230432 @default.
- W4310823043 hasOpenAccess W4310823043 @default.
- W4310823043 hasPrimaryLocation W43108230431 @default.
- W4310823043 hasRelatedWork W1765993298 @default.
- W4310823043 hasRelatedWork W178060743 @default.
- W4310823043 hasRelatedWork W2126676984 @default.
- W4310823043 hasRelatedWork W2141888607 @default.
- W4310823043 hasRelatedWork W2511178891 @default.
- W4310823043 hasRelatedWork W2753886513 @default.
- W4310823043 hasRelatedWork W2909390414 @default.
- W4310823043 hasRelatedWork W2954509079 @default.
- W4310823043 hasRelatedWork W3104472694 @default.
- W4310823043 hasRelatedWork W4384788979 @default.
- W4310823043 isParatext "false" @default.
- W4310823043 isRetracted "false" @default.
- W4310823043 workType "article" @default.