Most objects in the visual world are partially occluded, but humans can
recognize them without difficulty. However, it remains unknown whether object
recognition models like convolutional neural networks (CNNs) can handle
real-world occlusion. It is also a question whether efforts to make these
models robust to constant mask occlusion are effective for real-world
occlusion. We test both humans and the above-mentioned computational models in
a challenging task of object recognition under extreme occlusion, where target
objects are heavily occluded by irrelevant real objects in real backgrounds.
Our results show that human vision is very robust to extreme occlusion while
CNNs are not, even with modifications to handle constant mask occlusion. This
implies that the ability to handle constant mask occlusion does not entail
robustness to real-world occlusion. As a comparison, we propose another
computational model that utilizes object parts/subparts in a compositional
manner to build robustness to occlusion. This performs significantly better
than CNN-based models on our task with error patterns similar to humans. These
findings suggest that testing under extreme occlusion can better reveal the
robustness of visual recognition, and that the principle of composition can
encourage such robustness.Comment: To be presented at the 41st Annual Meeting of the Cognitive Science
Societ
