1 code implementation • CVPR 2022 • Rui Yu, Dawei Du, Rodney LaLonde, Daniel Davila, Christopher Funk, Anthony Hoogs, Brian Clipp
In this paper, we propose the Cascade Occluded Attention Transformer (COAT) for end-to-end person search.
1 code implementation • 9 Nov 2022 • Daniel Davila, Dawei Du, Bryon Lewis, Christopher Funk, Joseph Van Pelt, Roderick Collins, Kellie Corona, Matt Brown, Scott McCloskey, Anthony Hoogs, Brian Clipp
In this paper, we present the Multi-view Extended Videos with Identities (MEVID) dataset for large-scale, video person re-identification (ReID) in the wild.
no code implementations • ICCV 2017 • Christopher Funk, Yanxi Liu
Humans take advantage of real world symmetries for various tasks, yet capturing their superb symmetry perception mechanism with a computational model remains elusive.
no code implementations • 30 Nov 2018 • Christopher Funk, Savinay Nagendra, Jesse Scott, Bharadwaj Ravichandran, John H. Challis, Robert T. Collins, Yanxi Liu
In biomechanics, Center of Pressure (CoP) is used in studies of human postural control and gait.
no code implementations • 2 Jan 2020 • Jesse Scott, Christopher Funk, Bharadwaj Ravichandran, John H. Challis, Robert T. Collins, Yanxi Liu
To gain an understanding of the relation between a given human pose image and the corresponding physical foot pressure of the human subject, we propose and validate two end-to-end deep learning architectures, PressNet and PressNet-Simple, to regress foot pressure heatmaps (dynamics) from 2D human pose (kinematics) derived from a video frame.
no code implementations • ECCV 2020 • Jesse Scott, Bharadwaj Ravichandran, Christopher Funk, Robert T. Collins, Yanxi Liu
We propose and validate two end-to-end deep learning architectures to learn foot pressure distribution maps (dynamics) from 2D or 3D human pose (kinematics).
no code implementations • 12 Dec 2022 • Dawei Du, Ameya Shringi, Anthony Hoogs, Christopher Funk
Our approach uses the reconstruction error to determine the novelty of the video since unknown classes are harder to put back together and thus have a higher reconstruction error than videos from known classes.
no code implementations • 23 Dec 2022 • Derek S. Prijatelj, Samuel Grieggs, Jin Huang, Dawei Du, Ameya Shringi, Christopher Funk, Adam Kaufman, Eric Robertson, Walter J. Scheirer
Managing novelty in perception-based human activity recognition (HAR) is critical in realistic settings to improve task performance over time and ensure solution generalization outside of prior seen samples.
no code implementations • ICCV 2023 • Colorado J. Reed, Ritwik Gupta, Shufan Li, Sarah Brockman, Christopher Funk, Brian Clipp, Kurt Keutzer, Salvatore Candido, Matt Uyttendaele, Trevor Darrell
Large, pretrained models are commonly finetuned with imagery that is heavily augmented to mimic different conditions and scales, with the resulting models used for various tasks with imagery from a range of spatial scales.
no code implementations • 7 Mar 2023 • Christopher Funk, Yanxi Liu
A deep learning model, EscherNet 101, is constructed to categorize images of 2D periodic patterns into their respective 17 wallpaper groups.
no code implementations • CVPR 2023 • Chen Zhao, Dawei Du, Anthony Hoogs, Christopher Funk
Existing methods for open-set action recognition focus on novelty detection that assumes video clips show a single action, which is unrealistic in the real world.
no code implementations • 20 Jul 2023 • Christopher Funk, Ofer Dagan, Benjamin Noack, Nisar R. Ahmed
We then test our new non-monolithic CI algorithm on a large-scale target tracking simulation and show that it achieves a tighter bound and a more accurate estimate compared to the original monolithic CI.