8 citations · 8 across the 3 of their papers we have counts for
6 papers · 1 filter
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…
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…
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…
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…