Revealing the Proximate Long-Tail Distribution in Compositional
  Zero-Shot Learning
Chenyi Jiang Haofeng Zhang
Compositional Zero-Shot Learning (CZSL) aims to transfer knowledge from seen
state-object pairs to novel unseen pairs. In this process, visual bias caused
by the diverse interrelationship of state-object combinations blurs their
visual features, hindering the learning of distinguishable class prototypes.
Prevailing methods concentrate on disentangling states and objects directly
from visual features, disregarding potential enhancements that could arise from
a data viewpoint. Experimentally, we unveil the results caused by the above
problem closely approximate the long-tailed distribution. As a solution, we
transform CZSL into a proximate class imbalance problem. We mathematically
deduce the role of class prior within the long-tailed distribution in CZSL.
Building upon this insight, we incorporate visual bias caused by compositions
into the classifier's training and inference by estimating it as a proximate
class prior. This enhancement encourages the classifier to acquire more
discernible class prototypes for each composition, thereby achieving more
balanced predictions. Experimental results demonstrate that our approach
elevates the model's performance to the state-of-the-art level, without
introducing additional parameters. Our code is available at
\url{https://github.com/LanchJL/ProLT-CZSL}.