7 papers · 1 filter
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…
HANCLIP: A Family of Hyperbolic Angular Negation Vision Language Models
Hoang-Bao Le, Aiden Durrant, Thai Son Mai +3
Vision-Language Models (VLMs) are typically pre-trained on large-scale image-text datasets to capture semantic correspondences between visual content and natural language. However,…
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…
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…