no code implementations • 11 Jul 2022 • Jeffrey Byrne, Greg Castanon, Zhongheng Li, Gil Ettinger
We provide activity classification and activity detection benchmarks for this dataset, and analyze baseline results to gain insight into how people around with world perform common activities.
no code implementations • 1 Sep 2020 • Weidi Xie, Jeffrey Byrne, Andrew Zisserman
We describe three use cases on the public IJB-C face verification benchmark: (i) to improve 1:1 image-based verification error rates by rejecting low-quality face images; (ii) to improve quality score based fusion performance on the 1:1 set-based verification benchmark; and (iii) its use as a quality measure for selecting high quality (unblurred, good lighting, more frontal) faces from a collection, e. g. for automatic enrolment or display.
1 code implementation • 11 Aug 2020 • Jeffrey Byrne, Brian DeCann, Scott Bloom
Modern cameras are not designed with computer vision or machine learning as the target application.
1 code implementation • ECCV 2020 • Jonathan R. Williford, Brandon B. May, Jeffrey Byrne
Furthermore, we provide a comprehensive benchmark on this dataset comparing five state of the art methods for network attention in face recognition on three facial matchers.
1 code implementation • 28 Aug 2019 • Gregory Castanon, Nathan Shnidman, Tim Anderson, Jeffrey Byrne
The Out the Window (OTW) dataset is a crowdsourced activity dataset containing 5, 668 instances of 17 activities from the NIST Activities in Extended Video (ActEV) challenge.
no code implementations • 14 Apr 2017 • Daniel Crispell, Octavian Biris, Nate Crosswhite, Jeffrey Byrne, Joseph L. Mundy
The performance of modern face recognition systems is a function of the dataset on which they are trained.
no code implementations • 12 Mar 2016 • Nate Crosswhite, Jeffrey Byrne, Omkar M. Parkhi, Chris Stauffer, Qiong Cao, Andrew Zisserman
Face recognition performance evaluation has traditionally focused on one-to-one verification, popularized by the Labeled Faces in the Wild dataset for imagery and the YouTubeFaces dataset for videos.
Ranked #8 on Face Verification on IJB-A
no code implementations • CVPR 2015 • Jeffrey Byrne
A nested motion descriptor is a spatiotemporal representation of motion that is invariant to global camera translation, without requiring an explicit estimate of optical flow or camera stabilization.