Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Adaptive and Iteratively Improving Recurrent Lateral Connections   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.

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The categories of this paper are: q-fin
8
The categories of this paper are: hep-ex
19
The categories of this paper are: eess
30
The categories of this paper are: q-alg
36
The categories of this paper are: cmp-lg
11
The categories of this paper are: hep-lat
12
The categories of this paper are: nucl-th
29
The categories of this paper are: funct-an
10
The categories of this paper are: q-bio
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The categories of this paper are: econ
32
The categories of this paper are: atom-ph
2
The categories of this paper are: math
6
The categories of this paper are: astro-ph
9
The categories of this paper are: nlin
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The categories of this paper are: solv-int
27
The categories of this paper are: comp-gas