  Learning composite concepts, such as \textquotedbl red car\textquotedbl ,
from individual examples -- like a white car representing the concept of
\textquotedbl car\textquotedbl{} and a red strawberry representing the concept
of \textquotedbl red\textquotedbl -- is inherently challenging. This paper
introduces a novel method called Composite Concept Extractor (CoCE), which
leverages techniques from traditional backdoor attacks to learn these composite
concepts in a zero-shot setting, requiring only examples of individual
concepts. By repurposing the trigger-based model backdooring mechanism, we
create a strategic distortion in the manifold of the target object (e.g.,
\textquotedbl car\textquotedbl ) induced by example objects with the target
property (e.g., \textquotedbl red\textquotedbl ) from objects \textquotedbl red
strawberry\textquotedbl , ensuring the distortion selectively affects the
target objects with the target property. Contrastive learning is then employed
to further refine this distortion, and a method is formulated for detecting
objects that are influenced by the distortion. Extensive experiments with
in-depth analysis across different datasets demonstrate the utility and
applicability of our proposed approach.
