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  Dr Hui Wang  No more applications being accepted  Funded PhD Project (Students Worldwide)

About the Project

This PhD project will investigate multimodal video search by examples with a particular focus on video content representation, ranking and fusion using artificial intelligence techniques including deep learning.

This PhD project will support a large EPSRC funded multi-partner research project, Content-based Multimodal Video Search by Examples (MVSE), to develop new technologies to enable users to search efficiently and accurately for relevant content in multimedia archives. The MVSE project will use AI to interrogate all aspects of the multimedia content. For example, computer vision with deep learning will be used to interrogate each frame in a video for object identification and for face-based person identification; audio processing will be used for topic and context identification, as well as speaker identification. Using examples from each modality (visual, audio, text etc.), the system will provide multiple modality-specific content lists which are then ranked and fused to create a single content list. This project could revolutionise how users discover content in resources such as BBC Archives or YouTube.

This PhD project will focus on content representation, ranking and fusion in a multimodal video search context. It will investigate how to represent video content in an optimal way for search purposes, how to rank each of the modality specific content lists and how to fuse different ranked lists together taking into consideration user preferences such as diversity. The PhD candidate will work in a team of investigators and researchers at Queen’s and other partner institutions and will have opportunities to visit partner institutions.

Objectives:

  1. Deep learning framework for representing video contents for search purposes.
  2. Methodologies for efficiently ranking modality specific contents based on their representations.
  3. Methodologies for efficiently fusing different lists of contents based on user preferences.
  4. Research demonstrator prototype for video search.

Academic Requirements:

A minimum 2.1 honours degree or equivalent in Computer Science or Electrical and Electronic Engineering or relevant degree is required.

Applicants should apply electronically through the Queen’s online application portal at: https://dap.qub.ac.uk/portal/

Further information available at: https://www.qub.ac.uk/schools/eeecs/Research/PhDStudy/

Funding Notes:

This three year studentship, for full-time PhD study, is potentially funded by the Department for the Economy (DfE) and commences on 1 October 2022. For UK domiciled students the value of an award includes the cost of approved tuition fees as well as maintenance support (Fees £4,500 pa and Stipend rate £15,609 pa - 2022-23 rates to be confirmed). To be considered eligible for a full DfE studentship award you must have been ordinarily resident in the United Kingdom for the full three year period before the first day of the first academic year of the course.

For candidates who do not meet the above residency requirements, a small number of international studentships may be available from the School. These are expected to be highly competitive, and a selection process will determine the strongest candidates across a range of School projects, who may then be offered funding for their chosen project.


References

Multimodal Video Search by Examples (MVSE), funded by EPSRC, 1 April 2021 – 31 March 2024. EP/V002740/1.
Maria Dobrska, Hui Wang, William Blackburn (2011). Data-driven rank ordering -- a preference-based comparison study, Journal of Computational Intelligence Systems. 4(2):142-152.
Shuwei Chen, Jun Liu, Hui Wang, Juan Carlos Augusto (2013). Ordering based decision making – A survey. Information Fusion. 14:521-531.
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