Semantic Adversarial Network For Zero-shot Sketch-based Image Retrieval

Abstract

Zero-shot sketch-based image retrieval (ZS-SBIR) is a specific cross-modal retrieval task for retrieving natural images with free-hand sketches under zero-shot scenario. Previous works mostly focus on modeling the correspondence between images and sketches or synthesizing image features with sketch features. However, both of them ignore the large intra-class variance of sketches, thus resulting in unsatisfactory retrieval performance. In this paper, we propose a novel end-to-end semantic adversarial approach for ZS-SBIR. Specifically, we devise a semantic adversarial module to maximize the consistency between learned semantic features and category-level word vectors. Moreover, to preserve the discriminability of synthesized features within each training category, a triplet loss is employed for the generative module. Additionally, the proposed model is trained in an end-to-end strategy to exploit better semantic features suitable for ZS-SBIR. Extensive experiments conducted on two large

Stats

• citations26
• S2 citations—
• github stars0
• HF likes0
• heat score10.74
• arxiv keyxu2019semantic

Related papers

• Stacked Semantic-guided Network For Zero-shot Sketch-based Image Retrieval (2019)0.00
• Stacked Adversarial Network For Zero-shot Sketch Based Image Retrieval (2020)10.74
• Domain-smoothing Network For Zero-shot Sketch-based Image Retrieval (2021)13.92
• Relation-aware Meta-learning For Zero-shot Sketch-based Image Retrieval (2024)0.00
• Modality-aware Representation Learning For Zero-shot Sketch-based Image Retrieval (2024)8.60
• An Efficient Framework For Zero-shot Sketch-based Image Retrieval (2021)13.65
• Adapt And Align To Improve Zero-shot Sketch-based Image Retrieval (2023)0.00
• Zero-shot Everything Sketch-based Image Retrieval, And In Explainable Style (2023)16.67