118 citations · 121 across the 13 of their papers we have counts for
8 papers · 1 filter
HANCLIP: A Family of Hyperbolic Angular Negation Vision Language Models
Hoang-Bao Le, Aiden Durrant, Thai Son Mai +3
Vision-language models (VLMs) achieve strong cross-modal alignment but remain brittle to negation, often relying on shallow word associations rather than compositional reasoning. F…
HiAP: A Multi-Granular Stochastic Auto-Pruning Framework for Vision Transformers
Andy Li, Aiden Durrant, Milan Markovic +1
Vision Transformers require significant computational resources and memory bandwidth, severely limiting their deployment on resource-constraint hardware. Most structured pruning me…
EquiCaps: Predictor-Free Pose-Aware Pre-Trained Capsule Networks
Athinoulla Konstantinou, Georgios Leontidis, Mamatha Thota +1
Learning self-supervised representations that are invariant and equivariant to transformations is crucial for advancing beyond traditional visual classification tasks. However, man…
Monkey Transfer Learning Can Improve Human Pose Estimation
Bradley Scott, Clarisse de Vries, Aiden Durrant +3
In this study, we investigated whether transfer learning from macaque monkeys could improve human pose estimation. Current state-of-the-art pose estimation techniques, often employ…
Pushing the Limits of Sparsity: A Bag of Tricks for Extreme Pruning
Andy Li, Aiden Durrant, Milan Markovic +5
Pruning of deep neural networks has been an effective technique for reducing model size while preserving most of the performance of dense networks, crucial for deploying models on…
Capsule Network Projectors are Equivariant and Invariant Learners
Miles Everett, Aiden Durrant, Mingjun Zhong +1
Learning invariant representations has been the long-standing approach to self-supervised learning. However, recently progress has been made in preserving equivariant properties in…
HMSN: Hyperbolic Self-Supervised Learning by Clustering with Ideal Prototypes
Aiden Durrant, Georgios Leontidis
Hyperbolic manifolds for visual representation learning allow for effective learning of semantic class hierarchies by naturally embedding tree-like structures with low distortion w…
S-JEA: Stacked Joint Embedding Architectures for Self-Supervised Visual Representation Learning
Alžběta Manová, Aiden Durrant, Georgios Leontidis
The recent emergence of Self-Supervised Learning (SSL) as a fundamental paradigm for learning image representations has, and continues to, demonstrate high empirical success in a v…