  Although the human visual system is surprisingly robust to extreme distortion
when recognizing objects, most evaluations of computer object detection methods
focus only on robustness to natural form deformations such as people's pose
changes. To determine whether algorithms truly mirror the flexibility of human
vision, they must be compared against human vision at its limits. For example,
in Cubist abstract art, painted objects are distorted by object fragmentation
and part-reorganization, to the point that human vision often fails to
recognize them. In this paper, we evaluate existing object detection methods on
these abstract renditions of objects, comparing human annotators to four
state-of-the-art object detectors on a corpus of Picasso paintings. Our results
demonstrate that while human perception significantly outperforms current
methods, human perception and part-based models exhibit a similarly graceful
degradation in object detection performance as the objects become increasingly
abstract and fragmented, corroborating the theory of part-based object
representation in the brain.
