  We improve zero-shot learning (ZSL) by incorporating common-sense knowledge
in DNNs. We propose Common-Sense based Neuro-Symbolic Loss (CSNL) that
formulates prior knowledge as novel neuro-symbolic loss functions that
regularize visual-semantic embedding. CSNL forces visual features in the VSE to
obey common-sense rules relating to hypernyms and attributes. We introduce two
key novelties for improved learning: (1) enforcement of rules for a group
instead of a single concept to take into account class-wise relationships, and
(2) confidence margins inside logical operators that enable implicit curriculum
learning and prevent premature overfitting. We evaluate the advantages of
incorporating each knowledge source and show consistent gains over prior
state-of-art methods in both conventional and generalized ZSL e.g. 11.5%, 5.5%,
and 11.6% improvements on AWA2, CUB, and Kinetics respectively.
