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Found 8 papers, 3 papers with code
Fine-grained Activities of People Worldwide
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.
Inducing Predictive Uncertainty Estimation for Face Recognition
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.
Key-Nets: Optical Transformation Convolutional Networks for Privacy Preserving Vision Sensors
Modern cameras are not designed with computer vision or machine learning as the target application.
Explainable Face Recognition
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.
Out the Window: A Crowd-Sourced Dataset for Activity Classification in Security Video
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.
Dataset Augmentation for Pose and Lighting Invariant Face Recognition
The performance of modern face recognition systems is a function of the dataset on which they are trained.
Template Adaptation for Face Verification and Identification
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
Nested Motion Descriptors
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.
