Semantic-descriptor-based Generalized Zero-Shot Learning (GZSL) poses
challenges in recognizing novel classes in the test phase. The development of
generative models enables current GZSL techniques to probe further into the
semantic-visual link, culminating in a two-stage form that includes a generator
and a classifier. However, existing generation-based methods focus on enhancing
the generator's effect while neglecting the improvement of the classifier. In
this paper, we first analyze of two properties of the generated pseudo unseen
samples: bias and homogeneity. Then, we perform variational Bayesian inference
to back-derive the evaluation metrics, which reflects the balance of the seen
and unseen classes. As a consequence of our derivation, the aforementioned two
properties are incorporated into the classifier training as seen-unseen priors
via logit adjustment. The Zero-Shot Logit Adjustment further puts
semantic-based classifiers into effect in generation-based GZSL. Our
experiments demonstrate that the proposed technique achieves state-of-the-art
when combined with the basic generator, and it can improve various generative
Zero-Shot Learning frameworks. Our codes are available on
this https URL

本文提出了一种利用生成模型在 Generalized Zero-Shot Learning (GZSL) 任务中识别新类别的技术，使用变分贝叶斯推理方法推导评估指标，借助 logit adjustment 融合生成的伪类别信息到分类器中，进一步提高了在 GZSL 任务的性能。