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In this work, we study the challenging problem of identifying the irregularstatus of objects from images in an "open world" setting, that is,distinguishing the irregular status of an object category from its regularstatus as well as objects from other categories in the absence of "irregularobject" training data. To address this problem, we propose a novel approach byinspecting the distribution of the detection scores at multiple image regionsbased on the detector trained from the "regular object" and "other objects".The key observation motivating our approach is that for "regular object" imagesas well as "other objects" images, the region-level scores follow their ownessential patterns in terms of both the score values and the spatialdistributions while the detection scores obtained from an "irregular object"image tend to break these patterns. To model this distribution, we propose touse Gaussian Processes (GP) to construct two separate generative models for thecase of the "regular object" and the "other objects". More specifically, wedesign a new covariance function to simultaneously model the detection score ata single region and the score dependencies at multiple regions. We finallydemonstrate the superior performance of our method on a large dataset newlyproposed in this paper.

Hi Detector, What's Wrong with that Object? Identifying Irregular Object From Images by Modelling the Detection Score Distribution