Zero-Shot Sketch-Based Image Retrieval (ZS-SBIR) is a specific cross-modal retrieval task for searching natural images given free-hand sketches under the zero-shot scenario. Most existing methods solve this problem by simultaneously projecting visual features and semantic supervision into a low-dimensional common space for efficient retrieval. However, such low-dimensional projection destroys the completeness of semantic knowledge in original semantic space, so that it is unable to transfer useful knowledge well when learning semantic features from different modalities. Moreover, the domain information and semantic information are entangled in visual features, which is not conducive for cross-modal matching since it will hinder the reduction of domain gap between sketch and image. In this paper, we propose a Progressive Domain-independent Feature Decomposition (PDFD) network for ZS-SBIR. Specifically, with the supervision of original semantic knowledge, PDFD decomposes visual features into domain features and semantic ones, and then the semantic features are projected into common space as retrieval features for ZS-SBIR. The progressive projection strategy maintains strong semantic supervision. Besides, to guarantee the retrieval features to capture clean and complete semantic information, the cross-reconstruction loss is introduced to encourage that any combinations of retrieval features and domain features can reconstruct the visual features. Extensive experiments demonstrate the superiority of our PDFD over state-of-the-art competitors.

Zero-shot sketch-based image retrieval (ZS-SBIR) is a specific cross-modal retrieval task that involves searching natural images through the use of free-hand sketches under the zero-shot scenario. Most previous methods project the sketch and image features into a low-dimensional common space for efficient retrieval, and meantime align the projected features to their semantic features (e.g., category-level word vectors) in order to transfer knowledge from seen to unseen classes. However, the projection and alignment are always coupled; as a result, there is a lack of alignment that consequently leads to unsatisfactory zero-shot retrieval performance. To address this issue, we propose a novel progressive cross-modal semantic network. More specifically, it first explicitly aligns the sketch and image features to semantic features, then projects the aligned features to a common space for subsequent retrieval. We further employ cross-reconstruction loss to encourage the aligned features to capture complete knowledge about the two modalities, along with multi-modal Euclidean loss that guarantees similarity between the retrieval features from a sketch-image pair. Extensive experiments conducted on two popular large-scale datasets demonstrate that our proposed approach outperforms state-of-the-art competitors to a remarkable extent: by more than 3% on the Sketchy dataset and about 6% on the TU-Berlin dataset in terms of retrieval accuracy.