Domain-Smoothing Network for Zero-Shot Sketch-Based Image Retrieval
Zhipeng Wang, Hao Wang, Jiexi Yan, Aming Wu, Cheng Deng

TL;DR
This paper introduces a Domain-Smoothing Network that effectively reduces domain gap and intra-class diversity in zero-shot sketch-based image retrieval, significantly improving retrieval accuracy.
Contribution
The paper proposes a novel cross-modal contrastive learning approach and a category-specific memory bank to enhance ZS-SBIR performance, addressing key challenges neglected by prior methods.
Findings
Outperforms state-of-the-art on Sketchy and TU-Berlin datasets
Effectively reduces domain gap between sketches and images
Mitigates intra-class diversity in sketch representations
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…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsMultimodal Machine Learning Applications · Advanced Image and Video Retrieval Techniques · Domain Adaptation and Few-Shot Learning
