Generating generalized zero-shot learning based on dual-path feature enhancement

Generalized zero-shot learning (GZSL) can classify both seen and unseen class samples, which plays a significant role in practical applications such as emerging species recognition and medical image recognition. However, most existing GZSL methods directly use the pre-trained deep model to learn the image feature. Due to the data distribution inconsistency between the GZSL dataset and the pre-training dataset, the obtained image features have an inferior performance. The distribution of different class image features is similar, which makes them difficult to distinguish. To solve this problem, we propose a dual-path feature enhancement (DPFE) model, which consists of four modules: the feature generation network (FGN), the local fine-grained feature enhancement (LFFE) module, the global coarse-grained feature enhancement (GCFE) module, and the feedback module (FM). The feature generation network can synthesize unseen class image features. We enhance the image features' discriminative and semantic relevance from both local and global perspectives. To focus on the image's local discriminative regions, the LFFE module processes the image in blocks and minimizes the semantic cycle-consistency loss to ensure that the region block features contain key classification semantic information. To prevent information loss caused by image blocking, we design the GCFE module. It ensures the consistency between the global image features and the semantic centers, thereby improving the discriminative power of the features. In addition, the feedback module feeds the discriminator network's middle layer information back to the generator network. As a result, the synthesized image features are more similar to the real features. Experimental results demonstrate that the proposed DPFE method outperforms the state-of-the-arts on four zero-shot learning benchmark datasets.