  The recurring context in which objects appear holds valuable information that
can be employed to predict their existence. This intuitive observation indeed
led many researchers to endow appearance-based detectors with explicit
reasoning about context. The underlying thesis suggests that stronger
contextual relations would facilitate greater improvements in detection
capacity. In practice, however, the observed improvement in many cases is
modest at best, and often only marginal. In this work we seek to improve our
understanding of this phenomenon, in part by pursuing an opposite approach.
Instead of attempting to improve detection scores by employing context, we
treat the utility of context as an optimization problem: to what extent can
detection scores be improved by considering context or any other kind of
additional information? With this approach we explore the bounds on improvement
by using contextual relations between objects and provide a tool for
identifying the most helpful ones. We show that simple co-occurrence relations
can often provide large gains, while in other cases a significant improvement
is simply impossible or impractical with either co-occurrence or more precise
spatial relations. To better understand these results we then analyze the
ability of context to handle different types of false detections, revealing
that tested contextual information cannot ameliorate localization errors,
severely limiting its gains. These and additional insights further our
understanding on where and why utilization of context for object detection
succeeds and fails.
