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20202025
most citedA Review of Open-World Learning and Steps Toward Open-World Learning Without Labels

24 citations · 30 across the 5 of their papers we have counts for

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cs.CV2025

2COOOL: 2nd Workshop on the Challenge Of Out-Of-Label Hazards in Autonomous Driving

Ali K. AlShami, Ryan Rabinowitz, Maged Shoman +9

As the computer vision community advances autonomous driving algorithms, integrating vision-based insights with sensor data remains essential for improving perception, decision mak…

cs.CV2025

COSTARR: Consolidated Open Set Technique with Attenuation for Robust Recognition

Ryan Rabinowitz, Steve Cruz, Walter Scheirer +1

Handling novelty remains a key challenge in visual recognition systems. Existing open-set recognition (OSR) methods rely on the familiarity hypothesis, detecting novelty by the abs…

cs.CV2025

GHOST: Gaussian Hypothesis Open-Set Technique

Ryan Rabinowitz, Steve Cruz, Manuel Günther +1

Evaluations of large-scale recognition methods typically focus on overall performance. While this approach is common, it often fails to provide insights into performance across ind…

cs.CV2022

Enhanced Performance of Pre-Trained Networks by Matched Augmentation Distributions

Touqeer Ahmad, Mohsen Jafarzadeh, Akshay Raj Dhamija +4

There exists a distribution discrepancy between training and testing, in the way images are fed to modern CNNs. Recent work tried to bridge this gap either by fine-tuning or re-tra…

cs.CV2020★ 24 cited

A Review of Open-World Learning and Steps Toward Open-World Learning Without Labels

Mohsen Jafarzadeh, Akshay Raj Dhamija, Steve Cruz +3

In open-world learning, an agent starts with a set of known classes, detects, and manages things that it does not know, and learns them over time from a non-stationary stream of da…