Zero-shot learning(ZSL) aims to recognize new classes without prior exposure
to their samples, relying on semantic knowledge from observed classes. However,
current attention-based models may overlook the transferability of visual
features and the distinctiveness of attribute localization when learning
regional features in images. Additionally, they often overlook shared
attributes among different objects. Highly discriminative attribute features
are crucial for identifying and distinguishing unseen classes. To address these
issues, we propose an innovative approach called High-Discriminative Attribute
Feature Learning for Generalized Zero-Shot Learning (HDAFL). HDAFL optimizes
visual features by learning attribute features to obtain discriminative visual
embeddings. Specifically, HDAFL utilizes multiple convolutional kernels to
automatically learn discriminative regions highly correlated with attributes in
images, eliminating irrelevant interference in image features. Furthermore, we
introduce a Transformer-based attribute discrimination encoder to enhance the
discriminative capability among attributes. Simultaneously, the method employs
contrastive loss to alleviate dataset biases and enhance the transferability of
visual features, facilitating better semantic transfer between seen and unseen
classes. Experimental results demonstrate the effectiveness of HDAFL across
three widely used datasets.

零样本学习（ZSL）旨在根据观察到的类别的语义知识，识别未接触过的新类别，然而，当前基于注意力的模型对于学习图像区域特征时，可能忽视了视觉特征的可转移性和属性定位的独特性。为了解决这些问题，我们提出了一种名为高辨别属性特征学习的创新方法（HDAFL），通过学习属性特征来优化视觉特征以获取有辨识力的视觉嵌入。具体而言，HDAFL 利用多个卷积核自动学习与图像中属性高度相关的辨别性区域，消除图像特征中的不相关干扰。此外，我们引入了基于 Transformer 的属性判别编码器来增强属性之间的辨别能力。同时，该方法采用对比损失来减轻数据集偏差并增强视觉特征的可传递性，从而实现了观察类别和未观察类别之间更好的语义转移。实验结果在三个广泛使用的数据集上验证了 HDAFL 的有效性。