My name is Tom Smith, I am a third year PHD student in the Computer Vision Lab at the University of Nottingham. My current research is focused around improving learnt computer vision techniques with small amount of data. With an overall goal of creating a system to help paediatrician analyse the performance of resuscitation techniques on postnatal babies. Helping me are my four supervisors: Dr Michel Valstar, Dr Don Sharkey, Dr Jon Crowe, and Dr Mercedes Torres Torres.
Clinical Scene Segmentation with Tiny Datasets
Many clinical procedures could benefit from automatic scene segmentation and subsequent action recognition. Using Convolutional Neural Networks to semantically segment meaningful parts of an image or video is still an unsolved problem. This becomes even more apparent when only a small dataset is available. Whilst using RGB as the input is sufficient for a large labelled dataset, achieving high accuracy on a small dataset directly from RGB is difficult. This is because the ratio of free image dimensions to the number of training images is very high, resulting in unavoidable underfitting. We show that the addition of superpixels to represent an image in our network improves the semantic segmentation, and that superpixels can be learned to be detected by Convolutional Neural Networks if those superpixels are appropriately represented. Here we present a novel representation for superpixels, multichannel connected graphs (MCGs). We show how using pre-trained deep learned superpixels used in an end-to-end manner achieve good semantic segmentation results without the need for large quantities of labelled data, by only training with only 20 instances for 23 classes.
Computer Vision Representative for PGR-LCF (2017-Present)
The Learning Community Forum (LCF) aims to ensure that the views of the postgraduate research (PGR) students are given proper weight and that concerns they may have about supervision, progress, specific training, development opportunities, career, etc… are being addressed. Minutes of the Forum are taken into consideration in the review to promote a vibrant and thriving learning community for the students.
- Lead Demonstrator, G53MLE Machine Learning (2018-2019)
- Lab Assistant, G53MLE Machine Learning (2017-2018)
- Lab Assistant, G53GRA Computer Graphics (2016-2017)
I am one of the web chair for http://acii-conf.org/2019/
Annotated two emotion datasets for DynEmo
Annotated the LITTER dataset for the MCG work
Feel free to contact me at Thomas.Smith3@nottingham.ac.uk regarding anything related to my research.
If you need to find my at the university I am located at the following address:
B82 Computer Science Building
The University of Nottingham