  The single-hidden-layer Randomly Weighted Feature Network (RWFN) introduced
by Hong and Pavlic (2021) was developed as an alternative to neural tensor
network approaches for relational learning tasks. Its relatively small
footprint combined with the use of two randomized input projections -- an
insect-brain-inspired input representation and random Fourier features -- allow
it to achieve rich expressiveness for relational learning with relatively low
training cost. In particular, when Hong and Pavlic compared RWFN to Logic
Tensor Networks (LTNs) for Semantic Image Interpretation (SII) tasks to extract
structured semantic descriptions from images, they showed that the RWFN
integration of the two hidden, randomized representations better captures
relationships among inputs with a faster training process even though it uses
far fewer learnable parameters. In this paper, we use RWFNs to perform Visual
Relationship Detection (VRD) tasks, which are more challenging SII tasks. A
zero-shot learning approach is used with RWFN that can exploit similarities
with other seen relationships and background knowledge -- expressed with
logical constraints between subjects, relations, and objects -- to achieve the
ability to predict triples that do not appear in the training set. The
experiments on the Visual Relationship Dataset to compare the performance
between RWFNs and LTNs, one of the leading Statistical Relational Learning
frameworks, show that RWFNs outperform LTNs for the predicate-detection task
while using fewer number of adaptable parameters (1:56 ratio). Furthermore,
background knowledge represented by RWFNs can be used to alleviate the
incompleteness of training sets even though the space complexity of RWFNs is
much smaller than LTNs (1:27 ratio).
