Conference proceeding
Zero-Shot Sketch-Based Image Retrieval via Graph Convolution Network
THIRTY-FOURTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE, THE THIRTY-SECOND INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE AND THE TENTH AAAI SYMPOSIUM ON EDUCATIONAL ADVANCES IN ARTIFICIAL INTELLIGENCE, Vol.34(7), pp.12943-12950
AAAI Conference on Artificial Intelligence
04/03/2020
DOI: 10.1609/aaai.v34i07.6993
Abstract
Zero-Shot Sketch-based Image Retrieval (ZS-SBIR) has been proposed recently, putting the traditional Sketch-based Image Retrieval (SBIR) under the setting of zero-shot learning. Dealing with both the challenges in SBIR and zero-shot learning makes it become a more difficult task. Previous works mainly focus on utilizing one kind of information, i.e., the visual information or the semantic information. In this paper, we propose a SketchGCN model utilizing the graph convolution network, which simultaneously considers both the visual information and the semantic information. Thus, our model can effectively narrow the domain gap and transfer the knowledge. Furthermore, we generate the semantic information from the visual information using a Conditional Variational Autoencoder rather than only map them back from the visual space to the semantic space, which enhances the generalization ability of our model. Besides, feature loss, classification loss, and semantic loss are introduced to optimize our proposed SketchGCN model. Our model gets a good performance on the challenging Sketchy and TU-Berlin datasets.
Details
- Title: Subtitle
- Zero-Shot Sketch-Based Image Retrieval via Graph Convolution Network
- Creators
- Zhaolong Zhang - Fudan UniversityYuejie Zhang - Fudan UniversityRui Feng - Fudan UniversityTao Zhang - Shanghai University of Finance and EconomicsWeiguo Fan - University of Iowa
- Resource Type
- Conference proceeding
- Publication Details
- THIRTY-FOURTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE, THE THIRTY-SECOND INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE AND THE TENTH AAAI SYMPOSIUM ON EDUCATIONAL ADVANCES IN ARTIFICIAL INTELLIGENCE, Vol.34(7), pp.12943-12950
- Publisher
- Assoc Advancement Artificial Intelligence
- Series
- AAAI Conference on Artificial Intelligence
- DOI
- 10.1609/aaai.v34i07.6993
- ISSN
- 2159-5399
- eISSN
- 2374-3468
- Number of pages
- 8
- Grant note
- 17DZ1100504; 16JC1420401 / Shanghai Municipal RD Foundation 61976057; 61572140 / Naional Natural Science Foundation of China; National Natural Science Foundation of China (NSFC) 19ZR1417200 / Shanghai Natural Science Foundation; Natural Science Foundation of Shanghai 19YJA630116 / Humanities and Social Sciences Planning Fund of Ministry of Education of China Henry Tippie Endowed Chair Fund from the University of Iowa
- Language
- English
- Date published
- 04/03/2020
- Academic Unit
- Business Analytics
- Record Identifier
- 9984380552802771
Metrics
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