OCEAN: A Dual Learning Approach for Generalized Zero-Shot Sketch-Based Image Retrieval

Sketch-Based Image Retrieval (SBIR) is an emerging research area with many real-world applications. Recent studies have approached this research task under the more challenging zero-shot learning setting (ZS-SBIR), which assume classes in the target domain are unseen during the training stage. Many of the existing ZS-SBIR studies transferred the learned cross-modal (i.e., sketch and image) representations from the source domain to the target domain by leveraging side information in semantic embeddings. However, these ZS-SBIR methods are not able to generalize well to a more realistic setting to retrieve images from seen and unseen classes. To address the limitation of existing methods, we propose the cOmmon Conditional Encoder Adversarial Network (OCEAN) to perform generalized zero-shot sketch-based image retrieval (GZS-SBIR). The OCEAN model utilizes a dual learning framework to cyclically map the sketch and image features to a common semantic space, and project semantic features back to the relevant visual space by adversarial training. We conduct experiments on two publicly available datasets and demonstrate that our proposed model outperformed the state-of-the-arts baselines in both ZS-SBIR and GZS-SBIR tasks.