-
to develop automated AI-based workflows for video processing: a key bottleneck in upscaling video-based approaches. As a collaborative project between the University of Plymouth and Natural England
-
, temporal descriptions, prompt templates, and evidence-based explanations. Objectives We will investigate whether vision-language models (VLMs) can improve micro-expression analysis by combining video
-
of video and low-cost sensor technologies to capture subtle movement patterns, creating a rich dataset for AI-driven analysis. Machine learning, deep learning, computer vision and multimodal AI methods will
-
via feedback, interaction and continual learning. It will explore converting human videos, simulation, web-scale data and unstructured experience into supervision and reward signals through relabelling
-
vision, audio analysis and explainable AI methods. Rather than assuming that behavioural signals reveal personality, deception or suitability, the research will test whether any signals provide reliable
-
representations of Holmes (e.g. in illustration [Paget, Dorr Steele, and many others], film, radio, television, continuity novels, comics, graphic novels, video games [Frogwares], fine art or commercial graphic
-
(e.g. in illustration [Paget, Dorr Steele, and many others], film, radio, television, continuity novels, comics, graphic novels, video games [Frogwares], fine art or commercial graphic design); and (iii