Progressive Feature Reconstruction Network for Zero-Shot Learning
Linchun Hu, Wenming Cao, Zhenqi Zhang, Yuchuang Liang · IEEE Transactions on Circuits and Systems for Video Technology · 2025
Zero-shot learning (ZSL) aims to transfer the knowledge learned in the seen classes to the unseen classes through semantic knowledge. However, to ensure the model’s versatility on different datasets, existing methods divide the image into blocks of the same size, resulting in the loss of information between attributes. More importantly, existing methods ignore that not every image contains all attributes corresponding to that class. In this paper, we propose a progressive feature reconstruction network, called PFRN. PFRN consists of an attribute relation sub-net and an attention-based feature reconstruction sub-net. Specifically, the attribute relation sub-net first adopts the attribute-related region module to obtain the attribute-related regions in the visual features, which are input to the attribute relation discovery module to find the relationships between attributes. The attention-based feature reconstruction sub-net obtains the fine-grained features based on attributes by the attribute attention module and uses the feature reconstruction module to randomly lose some attributes to reconstruct the new visual features of the missing attributes. The new visual features are fed back into the network for training. Finally, the attribute information learned by the attribute relation sub-net is fused to the visual embedding learned by the attention-based features reconstruction sub-net, and the ideal visual semantic interaction is performed with the semantic vector classified by ZSL. Extensive experiments on three ZSL benchmark datasets demonstrate the significant generalization performance of our proposed method over the state-of-the-art methods.