The next PGR seminar is taking place this Friday 23rd May at 2PM in JC 1.33a

Below are the Titles and Abstracts for Thomas and Charis’ talks – Please do come along if you are able.

Thomas Martin

Title: From Isolated to Continuous Automated Sign Language Recognition

Abstract: Sign languages are full-fledged visual natural languages combining manual and non-manual features. As the name suggests, Automated Sign Language Understanding (ASLU) aims to automate tasks involving sign language. A primary obstacle to ASLU is the creation of appropriate datasets. Indeed, most datasets focus on materials gathered from TV broadcasts covering limited topics, which fail to accurately reflect sign language in the wild. Moreover, annotating such datasets is a prohibitively costly process. With the end goal of Sign Language Translation (written/spoken language to sign language) in mind, ASLU research has transitioned from Isolated to Continuous Sign Language Recognition. However, sign language intricacies have made this transition non-trivial.

Charis Hanna

Title: Enhancing Deep Learning Approaches for the Automated Monitoring of Dense Seabird Colonies

Abstract: Cliff-nesting birds serve as valuable indicators of marine ecosystem health, yet dense populations and remote habitats present significant challenges for automated monitoring. With current state-of-the-art object detectors often failing under the conditions of extreme crowding and occlusion, this project aims to develop and refine deep learning techniques that enable the fine-grained, automated analysis of seabird colonies. Current work explores semi-supervised learning strategies that leverage domain-shifted knowledge to reduce the need for exhaustive annotation across complex image datasets. These methods not only reduce the laborious process of manual annotation but also demonstrate promising improvements in performance across the long-tailed species distribution. While ongoing efforts are directed at further optimising these models, future work will leverage additional spatial information with the aim of supporting richer insights into behavioural dynamics within these populations.