  A split-transform-merge strategy has been broadly used as an architectural
constraint in convolutional neural networks for visual recognition tasks. It
approximates sparsely connected networks by explicitly defining multiple
branches to simultaneously learn representations with different visual concepts
or properties. Dependencies or interactions between these representations are
typically defined by dense and local operations, however, without any
adaptiveness or high-level reasoning. In this work, we propose to exploit this
strategy and combine it with our Visual Concept Reasoning Networks (VCRNet) to
enable reasoning between high-level visual concepts. We associate each branch
with a visual concept and derive a compact concept state by selecting a few
local descriptors through an attention module. These concept states are then
updated by graph-based interaction and used to adaptively modulate the local
descriptors. We describe our proposed model by
split-transform-attend-interact-modulate-merge stages, which are implemented by
opting for a highly modularized architecture. Extensive experiments on visual
recognition tasks such as image classification, semantic segmentation, object
detection, scene recognition, and action recognition show that our proposed
model, VCRNet, consistently improves the performance by increasing the number
of parameters by less than 1%.
