7 citations · 10 across the 7 of their papers we have counts for
7 papers
Beyond the Mud: Datasets and Benchmarks for Computer Vision in Off-Road Racing
Jacob Tyo, Motolani Olarinre, Youngseog Chung +1
Despite significant progress in optical character recognition (OCR) and computer vision systems, robustly recognizing text and identifying people in images taken in unconstrained \…
Contrastive Multiple Instance Learning for Weakly Supervised Person ReID
Jacob Tyo, Zachary C. Lipton
The acquisition of large-scale, precisely labeled datasets for person re-identification (ReID) poses a significant challenge. Weakly supervised ReID has begun to address this issue…
Reading Between the Mud: A Challenging Motorcycle Racer Number Dataset
Jacob Tyo, Youngseog Chung, Motolani Olarinre +1
This paper introduces the off-road motorcycle Racer number Dataset (RnD), a new challenging dataset for optical character recognition (OCR) research. RnD contains 2,411 images from…
MUDD: A New Re-Identification Dataset with Efficient Annotation for Off-Road Racers in Extreme Conditions
Jacob Tyo, Motolani Olarinre, Youngseog Chung +1
Re-identifying individuals in unconstrained environments remains an open challenge in computer vision. We introduce the Muddy Racer re-IDentification Dataset (MUDD), the first larg…
Meta-Learning Mini-Batch Risk Functionals
Jacob Tyo, Zachary C. Lipton
Supervised learning typically optimizes the expected value risk functional of the loss, but in many cases, we want to optimize for other risk functionals. In full-batch gradient de…
On the State of the Art in Authorship Attribution and Authorship Verification
Jacob Tyo, Bhuwan Dhingra, Zachary C. Lipton
Despite decades of research on authorship attribution (AA) and authorship verification (AV), inconsistent dataset splits/filtering and mismatched evaluation methods make it difficu…
How Transferable are the Representations Learned by Deep Q Agents?
Jacob Tyo, Zachary Lipton
In this paper, we consider the source of Deep Reinforcement Learning (DRL)'s sample complexity, asking how much derives from the requirement of learning useful representations of e…