  In real-world scenarios, typical visual recognition systems could fail under
two major causes, i.e., the misclassification between known classes and the
excusable misbehavior on unknown-class images. To tackle these deficiencies,
flexible visual recognition should dynamically predict multiple classes when
they are unconfident between choices and reject making predictions when the
input is entirely out of the training distribution. Two challenges emerge along
with this novel task. First, prediction uncertainty should be separately
quantified as confusion depicting inter-class uncertainties and ignorance
identifying out-of-distribution samples. Second, both confusion and ignorance
should be comparable between samples to enable effective decision-making. In
this paper, we propose to model these two sources of uncertainty explicitly
with the theory of Subjective Logic. Regarding recognition as an
evidence-collecting process, confusion is then defined as conflicting evidence,
while ignorance is the absence of evidence. By predicting Dirichlet
concentration parameters for singletons, comprehensive subjective opinions,
including confusion and ignorance, could be achieved via further evidence
combinations. Through a series of experiments on synthetic data analysis,
visual recognition, and open-set detection, we demonstrate the effectiveness of
our methods in quantifying two sources of uncertainties and dealing with
flexible recognition.
