Ontology Informed Machine Learning for Computer Vision
- Uli Sattler
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Other projects with the same supervisor
- Knowledge Graph for Guidance and Explainability in Machine Learning
- Controlled Synthesis of Virtual Patient Populations with Multimodal Representation Learning
- Trustworthy Multi-source Learning
- Representation Learning and Its Applications
- Machine Learning for Vision and Language Understanding
- Competition Funded Project (Students Worldwide)
This research project is one of a number of projects at this institution. It is in competition for funding with one or more of these projects. Usually the project which receives the best applicant will be awarded the funding. Applications for this project are welcome from suitably qualified candidates worldwide. Funding may only be available to a limited set of nationalities and you should read the full department and project details for further information.
This Ph.D. project will explore how we can make machine learning models aware of background knowledge in computer vision tasks, in the form of ontologies, with the aim of improving their performance, reducing the amount of training data required, increasing their robustness, and increasing their transparency.
Take to understand visual scenes as an example. A first step in scene understanding is the detection and recognition of objects from the input images, such as "person" or "bicycle", which is well-understood. A second step is the detection of visual relations between objects such as "person riding a bicycle" in the images, which is currently far more challenging. Machine learning models, such as neural networks, can in general be used to construct a mapping from an input image to a set of semantic relationships (or a scene graph) between the detected objects. However, success of state-of-the-art scene relation/graph extraction systems relies on the use of a very large amount of labeled image examples, called "ground truth". We could use an ontology to inform the vision model that a bicycle is a device with 2 wheels, and that a person can ride or push a bicycle. This project will build on existing work in the area (in particular a prior PhD project supervised by the same team, partially described in ) and investigate how the rich knowledge in an ontology can be injected into machine learning modelling.
This project combines the fields of machine learning, computer vision, and logic-based knowledge representation (ontologies and description logic), and aims to bring symbolic and neural approaches to AI together.
We will consider applicants who have:
* Some existing knowledge in some of the above fields, e.g., deep learning, computer vision, or ontologies and description logic.
* Enthusiasm for research across the above research fields.
* An excellent undergraduate degree in Computer Science or Mathematics (or related discipline), and preferably, a relevant M.Sc. degree.
* Very good experience with computer programming of mathematical models and algorithms (in Python, Matlab or other platforms).
* A willingness to investigate complex research problems carefully and to communicate the results of these investigations clearly both in spoken and written form.
* Enthusiasm for being part of the CS research school and engaging with its student and staff members.
*Some research experience, e.g., evidenced by well-written publications or project report, would be a plus.
Please, note that applicants must additionally satisfy the standard requirements for postgraduate studies at the University of Manchester, such as a first-class or high upper-second class (or an equivalent international qualification) and English language qualifications, as stated in the PGR guidelines.
Qualified applicants are strongly encouraged to informally contact the supervising academics Dr. Tingting Mu (firstname.lastname@example.org) or Prof. Uli Sattler (Uli.Sattler@manchester.ac.uk) to discuss the application and possible research directions prior to applying.
 Mirantha Jayathilaka, Tingting Mu, Uli Sattler, Towards Knowledge-aware Few-shot Learning with Ontology-based n-ball Concept Embeddings. ICMLA, 292-297, 2021.
Applicants will be required to address the following.
- Why do you want to do a PhD?
- In terms of personality and temperament, why do you believe you're suitable for doing a PhD and describe any experience that demonstrates your capacity to conduct research?
- How did you become interested in the ideas you mentioned in your research proposal?
- Outline the objectives of your research and explain the importance of this research in the context of your current knowledge?
- From your degree transcript what was your best and worst unit and why?
- What was your favourite unit and why?
- What was the most difficult part of your final year project and how did you overcome it?
- Describe how you have helped another with their learning either informally or formally or any service or leadership roles you might have had including extracurricular activities.
- Describe any community activities that you have been part of; such as hackathons, societies related to academics, or other extracurricular community activities for which you have participated in.
- How do you see your future after the PhD?