Multimodal Video Search by Examples
Lead Research Organisation:
University of Cambridge
Department Name: Engineering
Abstract
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People |
ORCID iD |
| Mark Gales (Principal Investigator) |
Publications
Liusie A
(2022)
University of Cambridge at TREC CAsT 2022
Ma R
(2023)
Adapting an Unadaptable ASR System
Ma R
(2023)
Adapting an Unadaptable ASR System
| Description | BBC R&D Demonstrate Partner |
| Organisation | British Broadcasting Corporation (BBC) |
| Country | United Kingdom |
| Sector | Public |
| PI Contribution | THE CUED MVSE team has developed underlying speech and text analysis tools for the BBC demonstrator. |
| Collaborator Contribution | BBC are the commercial partner for the MVSE project. They are involved in the development and evaluation of the MVSE demonstrator. Member of the BBC have been directly involved in supplying data, and specifying the scope of the project. |
| Impact | the initial release of the demonstrator to the BBC was made in April 2022. The initial release was not designed for formal evaluation, but to demonstrate a proof-of-concept. |
| Start Year | 2021 |
| Description | MMVC Workshop |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Other audiences |
| Results and Impact | This was a workshop held at BMVC 2022. he purpose was to bring together researchers on multimodal video search. The work shop was hybrid i nature with approximately 20 people attending in-person and 20 people on-line. |
| Year(s) Of Engagement Activity | 2022 |
| URL | https://mvse.ecit.qub.ac.uk |