Search Results for author:
Found 7 papers, 3 papers with code
Beyond the Mud: Datasets and Benchmarks for Computer Vision in Off-Road Racing
With these datasets and analysis of model limitations, we aim to foster innovations in handling real-world conditions like mud and complex poses to drive progress in robust computer vision.
Optical Character Recognition
Optical Character Recognition (OCR)
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Contrastive Multiple Instance Learning for Weakly Supervised Person ReID
Through extensive experiments and analysis across three datasets, CMIL not only matches state-of-the-art performance on the large-scale SYSU-30k dataset with fewer assumptions but also consistently outperforms all baselines on the WL-market1501 and Weakly Labeled MUddy racer re-iDentification dataset (WL-MUDD) datasets.
Reading Between the Mud: A Challenging Motorcycle Racer Number Dataset
RnD represents a valuable new benchmark to drive innovation in real-world OCR capabilities.
Optical Character Recognition
Optical Character Recognition (OCR)
MUDD: A New Re-Identification Dataset with Efficient Annotation for Off-Road Racers in Extreme Conditions
We analyze the impact of real-world factors including mud, pose, lighting, and more.
Meta-Learning Mini-Batch Risk Functionals
Supervised learning typically optimizes the expected value risk functional of the loss, but in many cases, we want to optimize for other risk functionals.
On the State of the Art in Authorship Attribution and Authorship Verification
Despite decades of research on authorship attribution (AA) and authorship verification (AV), inconsistent dataset splits/filtering and mismatched evaluation methods make it difficult to assess the state of the art.
How Transferable are the Representations Learned by Deep Q Agents?
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 environment states and how much is due to the sample complexity of learning a policy.
