  Work at the intersection of vision science and deep learning is starting to
explore the efficacy of deep convolutional networks (DCNs) and recurrent
networks in solving perceptual grouping problems that underlie primate visual
recognition and segmentation. Here, we extend this line of work to investigate
the compactness and generalizability of DCN solutions to learning low-level
perceptual grouping routines involving contour integration. We introduce V1Net,
a bio-inspired recurrent unit that incorporates lateral connections ubiquitous
in cortical circuitry. Feedforward convolutional layers in DCNs can be
substituted with V1Net modules to enhance their contextual visual processing
support for perceptual grouping. We compare the learning efficiency and
accuracy of V1Net-DCNs to that of 14 carefully selected feedforward and
recurrent neural architectures (including state-of-the-art DCNs) on MarkedLong
-- a synthetic forced-choice contour integration dataset of 800,000 images we
introduce here -- and the previously published Pathfinder contour integration
benchmarks. We gauged solution generalizability by measuring the transfer
learning performance of our candidate models trained on MarkedLong that were
fine-tuned to learn PathFinder. Our results demonstrate that a compact 3-layer
V1Net-DCN matches or outperforms the test accuracy and sample efficiency of all
tested comparison models which contain between 5x and 1000x more trainable
parameters; we also note that V1Net-DCN learns the most compact generalizable
solution to MarkedLong. A visualization of the temporal dynamics of a V1Net-DCN
elucidates its usage of interpretable grouping computations to solve
MarkedLong. The compact and rich representations of V1Net-DCN also make it a
promising candidate to build on-device machine vision algorithms as well as
help better understand biological cortical circuitry.
