Proceedings

Domain-Smoothing Network for Zero-Shot Sketch-Based Image Retrieval

Zhipeng Wang, Hao Wang, Jiexi Yan, Aming Wu, Cheng Deng

Download BibTeX Cite

Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence. Main Track. Pages 1143-1149.

https://doi.org/10.24963/ijcai.2021/158

Keywords

  • Machine Learning: Transfer, Adaptation, Multi-task Learning
  • Computer Vision: Recognition: Detection, Categorization, Indexing, Matching, Retrieval, Semantic Interpretation

Abstract

Zero-Shot Sketch-Based Image Retrieval (ZS-SBIR) is a novel cross-modal retrieval task, where abstract sketches are used as queries to retrieve natural images under zero-shot scenario. Most existing methods regard ZS-SBIR as a traditional classification problem and employ a cross-entropy or triplet-based loss to achieve retrieval, which neglect the problems of the domain gap between sketches and natural images and the large intra-class diversity in sketches. Toward this end, we propose a novel Domain-Smoothing Network (DSN) for ZS-SBIR. Specifically, a cross-modal contrastive method is proposed to learn generalized representations to smooth the domain gap by mining relations with additional augmented samples. Furthermore, a category-specific memory bank with sketch features is explored to reduce intra-class diversity in the sketch domain. Extensive experiments demonstrate that our approach notably outperforms the state-of-the-art methods in both Sketchy and TU-Berlin datasets.

Zhipeng Wang, Hao Wang, Jiexi Yan, Aming Wu, and Cheng Deng. 2021. Domain-Smoothing Network for Zero-Shot Sketch-Based Image Retrieval. In Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI-21. IJCAI, 1143–1149. https://doi.org/10.24963/ijcai.2021/158
Welcome Future Conferences AI Journal Division IJCAI Proceedings About IJCAI IJCAI Awards