5 research outputs found
Recurrent Connections Aid Occluded Object Recognition by Discounting Occluders
Recurrent connections in the visual cortex are thought to aid object
recognition when part of the stimulus is occluded. Here we investigate if and
how recurrent connections in artificial neural networks similarly aid object
recognition. We systematically test and compare architectures comprised of
bottom-up (B), lateral (L) and top-down (T) connections. Performance is
evaluated on a novel stereoscopic occluded object recognition dataset. The task
consists of recognizing one target digit occluded by multiple occluder digits
in a pseudo-3D environment. We find that recurrent models perform significantly
better than their feedforward counterparts, which were matched in parametric
complexity. Furthermore, we analyze how the network's representation of the
stimuli evolves over time due to recurrent connections. We show that the
recurrent connections tend to move the network's representation of an occluded
digit towards its un-occluded version. Our results suggest that both the brain
and artificial neural networks can exploit recurrent connectivity to aid
occluded object recognition.Comment: 13 pages, 5 figures, accepted at the 28th International Conference on
Artificial Neural Networks, published in Springer Lecture Notes in Computer
Science vol 1172
A computational exploration of complementary learning mechanisms in the primate ventral visual pathway
In order to develop transformation invariant representations of objects, the visual system must make use of constraints placed upon object transformation by the environment. For example, objects transform continuously from one point to another in both space and time. These two constraints have been exploited separately in order to develop translation and view invariance in a hierarchical multilayer model of the primate ventral visual pathway in the form of continuous transformation learning and temporal trace learning. We show for the first time that these two learning rules can work cooperatively in the model. Using these two learning rules together can support the development of invariance in cells and help maintain object selectivity when stimuli are presented over a large number of locations or when trained separately over a large number of viewing angles