  Compositional Zero-Shot Learning (CZSL) aims to recognize unseen compositions
from seen states and objects. The disparity between the manually labeled
semantic information and its actual visual features causes a significant
imbalance of visual deviation in the distribution of various object classes and
state classes, which is ignored by existing methods. To ameliorate these
issues, we consider the CZSL task as an unbalanced multi-label classification
task and propose a novel method called MUtual balancing in STate-object
components (MUST) for CZSL, which provides a balancing inductive bias for the
model. In particular, we split the classification of the composition classes
into two consecutive processes to analyze the entanglement of the two
components to get additional knowledge in advance, which reflects the degree of
visual deviation between the two components. We use the knowledge gained to
modify the model's training process in order to generate more distinct class
borders for classes with significant visual deviations. Extensive experiments
demonstrate that our approach significantly outperforms the state-of-the-art on
MIT-States, UT-Zappos, and C-GQA when combined with the basic CZSL frameworks,
and it can improve various CZSL frameworks. Our codes are available on
https://anonymous.4open.science/r/MUST_CGE/.
