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cs.CV2025

Preserving Angles Improves Feature Distillation

Evelyn J. Mannix, Liam Hodgkinson, Howard Bondell

Knowledge distillation methods compress models by training a student network using the classification outputs of a high quality teacher model, but can fail to effectively transfer…

cs.CV20251 cited

ComFe: An Interpretable Head for Vision Transformers

Evelyn J. Mannix, Liam Hodgkinson, Howard Bondell

Interpretable computer vision models explain their classifications through comparing the distances between the local embeddings of an image and a set of prototypes that represent t…

cs.CV2025

A Mixture of Exemplars Approach for Efficient Out-of-Distribution Detection with Foundation Models

Evelyn Mannix, Howard Bondell

One of the early weaknesses identified in deep neural networks trained for image classification tasks was their inability to provide low confidence predictions on out-of-distributi…

cs.CV2025

An interpretable approach to automating the assessment of biofouling in video footage

Evelyn J. Mannix, Bartholomew A. Woodham

Biofouling$\unicode{x2013}$communities of organisms that grow on hard surfaces immersed in water$\unicode{x2013}$provides a pathway for the spread of invasive marine species and di…

cs.CV2024

Detecting and recognizing characters in Greek papyri with YOLOv8, DeiT and SimCLR

Robert Turnbull, Evelyn Mannix

Purpose: The capacity to isolate and recognize individual characters from facsimile images of papyrus manuscripts yields rich opportunities for digital analysis. For this reason th…