  The current leading computer vision models are typically feed forward neural
models, in which the output of one computational block is passed to the next
one sequentially. This is in sharp contrast to the organization of the primate
visual cortex, in which feedback and lateral connections are abundant. In this
work, we propose a computational model for the role of lateral connections in a
given block, in which the weights of the block vary dynamically as a function
of its activations, and the input from the upstream blocks is iteratively
reintroduced. We demonstrate how this novel architectural modification can lead
to sizable gains in performance, when applied to visual action recognition
without pretraining and that it outperforms the literature architectures with
recurrent feedback processing on ImageNet.
