Abstract. In this paper, we propose the Broadcasting Convolutional
Network (BCN) that extracts key object features from the global field
of an entire input image and recognizes their relationship with local features. BCN is a simple network module that collects effective spatial
features, embeds location information and broadcasts them to the entire
feature maps. We further introduce the Multi-Relational Network (multiRN) that improves the existing Relation Network (RN) by utilizing the
BCN module. In pixel-based relation reasoning problems, with the help
of BCN, multiRN extends the concept of ‘pairwise relations’ in conventional RNs to ‘multiwise relations’ by relating each object with multiple
objects at once. This yields in O(n) complexity for n objects, which is
a vast computational gain from RNs that take O(n2
). Through experiments, multiRN has achieved a state-of-the-art performance on CLEVR
dataset, which proves the usability of BCN on relation reasoning problems