  In this work, we study the challenging problem of identifying the irregular
status of objects from images in an "open world" setting, that is,
distinguishing the irregular status of an object category from its regular
status as well as objects from other categories in the absence of "irregular
object" training data. To address this problem, we propose a novel approach by
inspecting the distribution of the detection scores at multiple image regions
based on the detector trained from the "regular object" and "other objects".
The key observation motivating our approach is that for "regular object" images
as well as "other objects" images, the region-level scores follow their own
essential patterns in terms of both the score values and the spatial
distributions while the detection scores obtained from an "irregular object"
image tend to break these patterns. To model this distribution, we propose to
use Gaussian Processes (GP) to construct two separate generative models for the
case of the "regular object" and the "other objects". More specifically, we
design a new covariance function to simultaneously model the detection score at
a single region and the score dependencies at multiple regions. We finally
demonstrate the superior performance of our method on a large dataset newly
proposed in this paper.
