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Computer Science > Computer Vision and Pattern Recognition

arXiv:2108.12115 (cs)
[Submitted on 27 Aug 2021 (v1), last revised 29 Oct 2021 (this version, v2)]

Title:Recognition Awareness: An Application of Latent Cognizance to Open-Set Recognition

Authors:Tatpong Katanyukul, Pisit Nakjai
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Abstract:This study investigates an application of a new probabilistic interpretation of a softmax output to Open-Set Recognition (OSR). Softmax is a mechanism wildly used in classification and object recognition.
However, a softmax mechanism forces a model to operate under a closed-set paradigm, i.e., to predict an object class out of a set of pre-defined labels.
This characteristic contributes to efficacy in classification, but poses a risk of non-sense prediction in object recognition.
Object recognition is often operated under a dynamic and diverse condition.
A foreign object -- an object of any unprepared class -- can be encountered at any time.
OSR is intended to address an issue of identifying a foreign object in object recognition.
Based on Bayes theorem and the emphasis of conditioning on the context, softmax inference has been re-interpreted.
This re-interpretation has led to a new approach to OSR, called Latent Cognizance (LC). Our investigation employs various scenarios, using Imagenet 2012 dataset as well as fooling and open-set images. The findings support LC hypothesis and show its effectiveness on OSR.
Comments: 27 pages
Subjects: Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2108.12115 [cs.CV]
  (or arXiv:2108.12115v2 [cs.CV] for this version)
  https://doi.org/10.48550/arXiv.2108.12115

Submission history

From: Tatpong Katanyukul [view email]
[v1] Fri, 27 Aug 2021 04:41:41 UTC (928 KB)
[v2] Fri, 29 Oct 2021 05:41:00 UTC (981 KB)
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