Liu, Fang, Deng, Xiaoming, Zou, Changqing, Lai, Yu-Kun  ORCID: https://orcid.org/0000-0002-2094-5680, Chen, Keqi, Zuo, Ran, Ma, Cuixia, Liu, Yong-Jin and Wang, Hongan
2022.
SceneSketcher-v2: Fine-grained scene-level sketch-based image retrieval using adaptive GCNs.
IEEE Transactions on Image Processing
31
, pp. 3737-3751.
10.1109/TIP.2022.3175403
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Abstract
Sketch-based image retrieval (SBIR) is a long-standing research topic in computer vision. Existing methods mainly focus on category-level or instance-level image retrieval. This paper investigates the fine-grained scene-level SBIR problem where a free-hand sketch depicting a scene is used to retrieve desired images. This problem is useful yet challenging mainly because of two entangled facts: 1) achieving an effective representation of the input query data and scene-level images is difficult as it requires to model the information across multiple modalities such as object layout, relative size and visual appearances, and 2) there is a great domain gap between the query sketch input and target images. We present SceneSketcher-v2, a Graph Convolutional Network (GCN) based architecture to address these challenges. SceneSketcher-v2 employs a carefully designed graph convolution network to fuse the multi-modality information in the query sketch and target images and uses a triplet training process and end-to-end training manner to alleviate the domain gap. Extensive experiments demonstrate SceneSketcher-v2 outperforms state-of-the-art scene-level SBIR models with a significant margin.
| Item Type: | Article |
|---|---|
| Date Type: | Publication |
| Status: | Published |
| Schools: | Schools > Computer Science & Informatics |
| Publisher: | Institute of Electrical and Electronics Engineers |
| ISSN: | 1057-7149 |
| Date of First Compliant Deposit: | 14 June 2022 |
| Date of Acceptance: | 29 April 2022 |
| Last Modified: | 26 Nov 2024 02:15 |
| URI: | https://orca.cardiff.ac.uk/id/eprint/150486 |
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