  Zero-shot object navigation (ZSON) addresses situation where an agent
navigates to an unseen object that does not present in the training set.
Previous works mainly train agent using seen objects with known labels, and
ignore the seen objects without labels. In this paper, we introduce seen
objects without labels, herein termed as ``unknown objects'', into training
procedure to enrich the agent's knowledge base with distinguishable but
previously overlooked information. Furthermore, we propose the label-wise
meta-correlation module (LWMCM) to harness relationships among objects with and
without labels, and obtain enhanced objects information. Specially, we propose
target feature generator (TFG) to generate the features representation of the
unlabeled target objects. Subsequently, the unlabeled object identifier (UOI)
module assesses whether the unlabeled target object appears in the current
observation frame captured by the camera and produces an adapted target
features representation specific to the observed context. In meta contrastive
feature modifier (MCFM), the target features is modified via approaching the
features of objects within the observation frame while distancing itself from
features of unobserved objects. Finally, the meta object-graph learner (MOGL)
module is utilized to calculate the relationships among objects based on the
features. Experiments conducted on AI2THOR and RoboTHOR platforms demonstrate
the effectiveness of our proposed method.
