75 citations · 127 across the 8 of their papers we have counts for
10 papers
Face, Whole-Person, and Object Classification in a Unified Space Via The Interleaved Multi-Domain Identity Curriculum
Thomas M Metz, Matthew Q Hill, Alice J O'Toole
Vision foundation models can perform generalized object classification in zero-shot mode, and face/person recognition when they are fine-tuned. However, fine-tuned models suffer fr…
Not All Starting Points Are Equal: Pre-trained Priors and Their Outsized Impact on Person Identification
Thomas M. Metz, Matthew Q. Hill, Alice J. O'Toole
Recent years have seen an explosion of diverse general purpose pre-training methodologies for computer vision. However, the impact that these pre-training methodologies have on per…
Dissecting Human Body Representations in Deep Networks Trained for Person Identification
Thomas M Metz, Matthew Q Hill, Blake Myers +3
Long-term body identification algorithms have emerged recently with the increased availability of high-quality training data. We seek to fill knowledge gaps about these models by a…
Unconstrained Body Recognition at Altitude and Range: Comparing Four Approaches
Blake A Myers, Matthew Q Hill, Veda Nandan Gandi +2
This study presents an investigation of four distinct approaches to long-term person identification using body shape. Unlike short-term re-identification systems that rely on tempo…
Recognizing People by Body Shape Using Deep Networks of Images and Words
Blake A. Myers, Lucas Jaggernauth, Thomas M. Metz +4
Common and important applications of person identification occur at distances and viewpoints in which the face is not visible or is not sufficiently resolved to be useful. We exami…
Single Unit Status in Deep Convolutional Neural Network Codes for Face Identification: Sparseness Redefined
Connor J. Parde, Y. Ivette Colón, Matthew Q. Hill +3
Deep convolutional neural networks (DCNNs) trained for face identification develop representations that generalize over variable images, while retaining subject (e.g., gender) and…
P2L: Predicting Transfer Learning for Images and Semantic Relations
Bishwaranjan Bhattacharjee, John R. Kender, Matthew Hill +7
Transfer learning enhances learning across tasks, by leveraging previously learned representations -- if they are properly chosen. We describe an efficient method to accurately est…
Deep Convolutional Neural Networks in the Face of Caricature: Identity and Image Revealed
Matthew Q. Hill, Connor J. Parde, Carlos D. Castillo +5
Real-world face recognition requires an ability to perceive the unique features of an individual face across multiple, variable images. The primate visual system solves the problem…
Improving Transferability of Deep Neural Networks
Parijat Dube, Bishwaranjan Bhattacharjee, Elisabeth Petit-Bois +1
Learning from small amounts of labeled data is a challenge in the area of deep learning. This is currently addressed by Transfer Learning where one learns the small data set as a t…
Deep Convolutional Neural Network Features and the Original Image
Connor J. Parde, Carlos Castillo, Matthew Q. Hill +4
Face recognition algorithms based on deep convolutional neural networks (DCNNs) have made progress on the task of recognizing faces in unconstrained viewing conditions. These netwo…