[visionlist] [Jobs] A fully-funded 4-year PhD scholarship in Computer Vision and Machine Learning

[jungong han]

*A fully-funded 4-year PhD scholarship* *in UKRI Centre for Doctoral
Training in Artificial Intelligence, Machine Learning & Advanced Computing*

*Project:* Few-shot Learning for Environment Adaptive Multi-modal Vision
System



As a dominating technique in AI, deep learning has been successfully used
to facilitate a multitude of visual tasks, such as recognizing faces,
tracking emotions, or monitoring physical activities. However, each of
these tasks requires training a neural network on a *very large image
dataset* specifically collected and annotated for that task. Though the
trained networks are experts for the target task, they only understand the
'world' experienced during training and can 'say' nothing about other
content, nor can they be adaptive to other tasks without retraining.
Moreover, most visual algorithms are learning from ‘single modal’, but pay
no attention to other vision modalities, such as depth and thermal sensors.



The core objective of the project is to develop the next generation of
machine learning algorithms that can mimic human vision and intelligence –
continually learning to adapt to the new environment from *a few visual
shots* without requiring the traditional ‘strong supervision’ of a new
dataset of each new task. Compared to the conventional supervised setting,
learning from few shots poses several challenges due to, e. g. insufficient
training data in a new environment; the bias between old and new tasks; the
continually emerging tasks; the catastrophic forgetting problem when
learning new tasks. In this project, the prospective student will solve the
above problems in three stages: 1) based on our recent findings of
self-paced (curriculum) learning for image classification [1] and saliency
detection [2], we address the lack of annotated training data problem for
real-life semantic segmentation task; 2) incorporating self-paced learning
into the framework of Meta-learning, aiming to be environment adaptive by
learning emerging new tasks data from old tasks; 3) delivering, distilling,
and reasoning semantic and geometric information over a multitude of visual
data, e.g. RGB videos, depth videos and thermal videos.



[1] L. Xiang, G. Ding and *J. Han*, Learning from Multiple Experts:
Self-paced Knowledge Distillation for Long-tailed Classification, in
proceeding of European Conference on Computer Vision (ECCV 2020), spotlight
paper (top 5%).

[2] D. Zhang, H. Tian and *J. Han*, Few-Cost Salient Object Detection with
Adversarial-Paced Learning, in proceeding of 34th Conference on Neural
Information Processing Systems (NeurIPS 2020).



*Eligibility and Desired Student background*

We are seeking an enthusiastic individual to join the Computer Science
Department at Aberystwyth University, UK, with the following attributes:

·         A minimum 2:1 undergraduate (BEng, MEng) and/or postgraduate
masters’ qualification (MSc) in a science and technology field: Computer
Science, Engineering, Mathematics, with specialisation in Computer Vision,
Machine Learning and AI

·         Appropriate IELTS score (overall score of 6.0 with no component
below 5.5) or TOEFL.

·         Familiarity with machine learning and probabilistic models

·         Relevant software knowledge and experience, for example Python
and tensor frameworks (PyTorch or TensorFlow), C++, etc

·         A driven, professional and independent work attitude

·         Excellent written and verbal communication skills

*Funding Notes*

·         The 4-year PhD scholarship, will sit within the UKRI Centre for
Doctoral Training in Artificial Intelligence, Machine Learning & Advanced
Computing

·         Funding will cover the full cost of tuition fees and an annual
stipend of £15,285 *for 4 years*

·         The post is open to both Home/EU and overseas students

·         Additional funding is available for training, research and
conference expenses



*How to Apply:*

Applications through Aberystwyth’s electronic application process *Aberystwyth
University - Postgraduate Study : How to apply
<https://www.aber.ac.uk/en/postgrad/apply/#apply-online>* must include the
following attachments in pdf form:

1. CV
2. Degree certificates and transcripts (if you are still an undergraduate,
provide a transcript of results known to date)
3. A statement no longer than 1000 words that explains why you want to join
our Centre, and your preferred topic/supervisor.
4. Academic references - all scholarship applications require two
supporting references to be submitted. Please ensure that your chosen
referees are aware of the funding deadline (to be determined), as their
references form a vital part of the evaluation process. Please include
these with your scholarship application.

In addition, email the pdf(s) of your application to Prof. Jungong Han [
jungong.han at aber.ac.uk].

The deadline for applications is *12 February 2021*, and the start date is
Oct. 2021.

*Supervisors:*

Prof. Jungong Han



Prof. Qiang Shen
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