Search Results for author: Jack Valmadre

Found 13 papers, 5 papers with code

On progressive sharpening, flat minima and generalisation

no code implementations24 May 2023 Lachlan Ewen MacDonald, Jack Valmadre, Simon Lucey

We present a new approach to understanding the relationship between loss curvature and generalisation in deep learning.

Hierarchical classification at multiple operating points

1 code implementation19 Oct 2022 Jack Valmadre

Many classification problems consider classes that form a hierarchy.

General Classification

A global analysis of global optimisation

no code implementations10 Oct 2022 Lachlan Ewen MacDonald, Hemanth Saratchandran, Jack Valmadre, Simon Lucey

We introduce a general theoretical framework, designed for the study of gradient optimisation of deep neural networks, that encompasses ubiquitous architectural choices including batch normalisation, weight normalisation and skip connections.

Devon: Deformable Volume Network for Learning Optical Flow

no code implementations20 Feb 2018 Yao Lu, Jack Valmadre, Heng Wang, Juho Kannala, Mehrtash Harandi, Philip H. S. Torr

State-of-the-art neural network models estimate large displacement optical flow in multi-resolution and use warping to propagate the estimation between two resolutions.

Optical Flow Estimation

Fully-Convolutional Siamese Networks for Object Tracking

9 code implementations30 Jun 2016 Luca Bertinetto, Jack Valmadre, João F. Henriques, Andrea Vedaldi, Philip H. S. Torr

The problem of arbitrary object tracking has traditionally been tackled by learning a model of the object's appearance exclusively online, using as sole training data the video itself.

object-detection Object Detection +1

Staple: Complementary Learners for Real-Time Tracking

3 code implementations CVPR 2016 Luca Bertinetto, Jack Valmadre, Stuart Golodetz, Ondrej Miksik, Philip Torr

Correlation Filter-based trackers have recently achieved excellent performance, showing great robustness to challenging situations exhibiting motion blur and illumination changes.

regression Visual Object Tracking

Dense Semantic Correspondence where Every Pixel is a Classifier

no code implementations ICCV 2015 Hilton Bristow, Jack Valmadre, Simon Lucey

Determining dense semantic correspondences across objects and scenes is a difficult problem that underpins many higher-level computer vision algorithms.

Object Recognition Semantic correspondence

Learning detectors quickly using structured covariance matrices

no code implementations28 Mar 2014 Jack Valmadre, Sridha Sridharan, Simon Lucey

Computer vision is increasingly becoming interested in the rapid estimation of object detectors.

Cannot find the paper you are looking for? You can Submit a new open access paper.