236 citations · 296 across the 11 of their papers we have counts for
14 papers
An accurate detection is not all you need to combat label noise in web-noisy datasets
Paul Albert, Jack Valmadre, Eric Arazo +3
Training a classifier on web-crawled data demands learning algorithms that are robust to annotation errors and irrelevant examples. This paper builds upon the recent empirical obse…
On progressive sharpening, flat minima and generalisation
Lachlan Ewen MacDonald, Jack Valmadre, Simon Lucey
We present a new approach to understanding the relationship between loss curvature and input-output model behaviour in deep learning. Specifically, we use existing empirical analys…
Hierarchical classification at multiple operating points
Jack Valmadre
Many classification problems consider classes that form a hierarchy. Classifiers that are aware of this hierarchy may be able to make confident predictions at a coarse level despit…
On skip connections and normalisation layers in deep optimisation
Lachlan Ewen MacDonald, Jack Valmadre, Hemanth Saratchandran +1
We introduce a general theoretical framework, designed for the study of gradient optimisation of deep neural networks, that encompasses ubiquitous architecture choices including ba…
Learning with Neighbor Consistency for Noisy Labels
Ahmet Iscen, Jack Valmadre, Anurag Arnab +1
Recent advances in deep learning have relied on large, labelled datasets to train high-capacity models. However, collecting large datasets in a time- and cost-efficient manner ofte…
Local Metrics for Multi-Object Tracking
Jack Valmadre, Alex Bewley, Jonathan Huang +3
This paper introduces temporally local metrics for Multi-Object Tracking. These metrics are obtained by restricting existing metrics based on track matching to a finite temporal ho…
Long-term Tracking in the Wild: A Benchmark
Jack Valmadre, Luca Bertinetto, João F. Henriques +5
We introduce the OxUvA dataset and benchmark for evaluating single-object tracking algorithms. Benchmarks have enabled great strides in the field of object tracking by defining sta…
Devon: Deformable Volume Network for Learning Optical Flow
Yao Lu, Jack Valmadre, Heng Wang +3
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. Despite the…
End-to-end representation learning for Correlation Filter based tracking
Jack Valmadre, Luca Bertinetto, João F. Henriques +2
The Correlation Filter is an algorithm that trains a linear template to discriminate between images and their translations. It is well suited to object tracking because its formula…
Learning feed-forward one-shot learners
Luca Bertinetto, João F. Henriques, Jack Valmadre +2
One-shot learning is usually tackled by using generative models or discriminative embeddings. Discriminative methods based on deep learning, which are very effective in other learn…
Fully-Convolutional Siamese Networks for Object Tracking
Luca Bertinetto, Jack Valmadre, João F. Henriques +2
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 its…
Staple: Complementary Learners for Real-Time Tracking
Luca Bertinetto, Jack Valmadre, Stuart Golodetz +2
Correlation Filter-based trackers have recently achieved excellent performance, showing great robustness to challenging situations exhibiting motion blur and illumination changes.…
Dense Semantic Correspondence where Every Pixel is a Classifier
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. Unlike canonical dense corr…
Learning detectors quickly using structured covariance matrices
Jack Valmadre, Sridha Sridharan, Simon Lucey
Computer vision is increasingly becoming interested in the rapid estimation of object detectors. Canonical hard negative mining strategies are slow as they require multiple passes…