USING CLASSIFIER DISCREPANCY FOR CROSS-DOMAIN IMAGE RETRIEVAL

In recent years, cross-domain image retrieval (CDIM) has garnered considerable interest. The primary difficulty of CDIM is the domain gap, which makes it hard for the system to retrieve two photos that belong to the same category but have distinct domains. In this paper, we provide a novel multi-branch network employing the quintuplet structure to minimize retrieval loss and classifier discrepancy to minimize domain loss. Using three public datasets, we test the proposed method for zero-shot sketch-based image retrieval, which is one of CDIM's application tasks. Experiments validated the proposed method's state-of-the-art performance on the majority of datasets.