Search Results for author: Jack Valmadre

Found 10 papers, 3 papers with code

Learning with Neighbor Consistency for Noisy Labels

no code implementations29 Sep 2021 Ahmet Iscen, Jack Valmadre, Anurag Arnab, Cordelia Schmid

However, collecting large datasets in a time- and cost-efficient manner often results in label noise.

Image Classification

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

6 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 Tracking

Staple: Complementary Learners for Real-Time Tracking

2 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.

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.

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