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will implement a network of video cameras to investigate fine-scale patterns of habitat use over extended periods, in relation to key environmental drivers. This camera network will also act as a testbed
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, 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
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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
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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
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judgement. Design ethically approved protocols for collecting and analysing interview, audio, video and assessment data. Develop interpretable multimodal AI methods and test whether they provide reliable
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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
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(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