4 accepted papers
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 absence of familiar features. We propose a novel attenuation hypothesis: small weights learned during training attenuate fe…
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 individual classes, which can lead to fairness issues and misrepresentation. Addressing these gaps is crucial for accuratel…
"Open-Set Recognition (OSR) is a problem with mainly practical applications. However, recent evaluations have largely focused on small-scale data and tuning thresholds over the test set, which disregard the real-world operational needs of parameter selection. Thus, we revisit the original goals of O…
Deep networks have produced significant gains for various visual recognition problems, leading to high impact academic and commercial applications. Recent work in deep networks highlighted that it is easy to generate images that humans would never classify as a particular object class, yet networks…