Sharing Model Framework for Zero-Shot Sketch-Based Image Retrieval

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dc.contributor.authorHo, Yi-Hsuanen_US
dc.contributor.authorWay, Der-Loren_US
dc.contributor.authorShih, Zen-Chungen_US
dc.contributor.editorChaine, Raphaëlleen_US
dc.contributor.editorDeng, Zhigangen_US
dc.contributor.editorKim, Min H.en_US
dc.date.accessioned2023-10-09T07:34:42Z
dc.date.available2023-10-09T07:34:42Z
dc.date.issued2023
dc.description.abstractSketch-based image retrieval (SBIR) is an emerging task in computer vision. Research interests have arisen in solving this problem under the realistic and challenging setting of zero-shot learning. Given a sketch as a query, the search goal is to retrieve the corresponding photographs in a zero-shot scenario. In this paper, we divide the aforementioned challenging work into three tasks and propose a sharing model framework that addresses these problems. First, the weights of the proposed sharing model effectively reduced the modality gap between sketches and photographs. Second, semantic information was used to handle different label spaces during the training and testing stages. The sketch and photograph domains share semantic information. Finally, a memory mechanism is used to reduce the intrinsic variety in sketches, even if they all belong to the same class. Sketches and photographs dominate the embeddings in turn. Because sketches are not limited by language, our ultimate goal is to find a method to replace text searches. We also designed a demonstration program to demonstrate the use of the proposed method in real-world applications. Our results indicate that the proposed method exhibits considerably higher zero-shot SBIR performance than do other state-of-the-art methods on the challenging Sketchy, TU-Berlin, and QuickDraw datasets.en_US
dc.description.number7
dc.description.sectionheadersSketch-based Modeling
dc.description.seriesinformationComputer Graphics Forum
dc.description.volume42
dc.identifier.doi10.1111/cgf.14947
dc.identifier.issn1467-8659
dc.identifier.pages12 pages
dc.identifier.urihttps://doi.org/10.1111/cgf.14947
dc.identifier.urihttps://diglib.eg.org:443/handle/10.1111/cgf14947
dc.publisherThe Eurographics Association and John Wiley & Sons Ltd.en_US
dc.subjectCCS Concepts: Information system -> Information retrieval; Computing methodologies -> Machine learning
dc.subjectInformation system
dc.subjectInformation retrieval
dc.subjectComputing methodologies
dc.subjectMachine learning
dc.titleSharing Model Framework for Zero-Shot Sketch-Based Image Retrievalen_US
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