Progressive Domain-Independent Feature Decomposition Network for Zero-Shot Sketch-Based Image Retrieval

• URL: http://arxiv.org/abs/2003.09869v2
• Date: Fri, 6 May 2022 12:07:01 GMT
• Title: Progressive Domain-Independent Feature Decomposition Network for Zero-Shot Sketch-Based Image Retrieval
• Authors: Xinxun Xu, Muli Yang, Yanhua Yang and Hao Wang
• Abstract summary: We propose a Progressive Domain-independent Feature Decomposition (PDFD) network for ZS-SBIR. Specifically, 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.
• Score: 15.955284712628444
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 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.

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