Generalized Zero-Shot Image Classification Based on Reconstruction Contrast

Abstract

In generalized zero-shot image classification, generative models are often exploited to reconstruct visual or semantic information for further learning. However, the representation performance of the methods based on variational autoencoders is poor due to the underutilization of the reconstructed samples. Therefore, a generalized zero-shot image classification model based on reconstruction and contrastive learning is proposed. Firstly, two variational self-encoders are utilized to encode visual information and semantic information into low dimensional latent vectors of the same dimension, and then the latent vectors are decoded into two modes respectively. Next, the project modules are utilized to project both the original visual information and the visual information reconstructed from semantic modal latent vectors. Then, reconstruction contrastive learning is performed to learn the features after projection. The reconstruction performance of the encoder is maintained, the discriminative performance of the encoder is enhanced, and the application ability of pre-training features on the generalized zero-shot task is improved by the proposed method. The effectiveness of the proposed model is verified on four benchmark datasets.