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

• URL: http://arxiv.org/abs/2108.12115v1
• Date: Fri, 27 Aug 2021 04:41:41 GMT
• Title: Recognition Awareness: An Application of Latent Cognizance to Open-Set Recognition
• Authors: Tatpong Katanyukul and Pisit Nakjai
• Abstract summary: Softmax mechanism forces a model 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. Open-Set Recognition is intended to address an issue of identifying a foreign object in object recognition.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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