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	Deep Multimodal Embedding Model for Fine-grained Sketch-based Image Retrieval
	Huang, Fei1; Cheng, Yong1; Jin, Cheng1; Zhang, Yuejie1; Zhang, Tao2
	2017
关键词	Fine-grained Sketch-based Image Retrieval (Fine-grained SBIR) Deep Multimodal Embedding Multimodal Ranking Loss
DOI	10.1145/3077136.3080681
页码	929-932
英文摘要	Fine-grained Sketch-based Image Retrieval (Fine-grained SBIR), which uses hand-drawn sketches to search the target object images, has been an emerging topic over the last few years. The difficulties of this task not only come from the ambiguous and abstract characteristics of sketches with less useful information, but also the cross-modal gap at both visual and semantic level. However, images on the web are always exhibited with multimodal contents. In this paper, we consider Fine-grained SBIR as a cross-modal retrieval problem and propose a deep multimodal embedding model that exploits all the beneficial multimodal information sources in sketches and images. In our experiment with large quantity of public data, we show that the proposed method outperforms the state-of-the-art methods for Fine-grained SBIR.
会议录出版者	ASSOC COMPUTING MACHINERY
会议录出版地	1515 BROADWAY, NEW YORK, NY 10036-9998 USA
语种	英语
WOS研究方向	Computer Science
WOS记录号	WOS:000454711900115
内容类型	会议论文
源URL	[http://10.2.47.112/handle/2XS4QKH4/2996]
专题	上海财经大学
作者单位	1.Fudan Univ, Shanghai Key Lab Intelligent Informat Proc, Sch Comp Sci, Shanghai, Peoples R China; 2.Shanghai Univ Finance & Econ, Sch Informat Management & Engn, Shanghai, Peoples R China
推荐引用方式 GB/T 7714	Huang, Fei,Cheng, Yong,Jin, Cheng,et al. Deep Multimodal Embedding Model for Fine-grained Sketch-based Image Retrieval[C]. 见:.

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