Learning Attribute Representations with Localization for Flexible Fashion Search
| Autor: | Kenan E. Ak, Ashraf A. Kassim, Joo-Hwee Lim, Jo Yew Tham |
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| Rok vydání: | 2018 |
| Předmět: |
Information retrieval
business.industry Computer science Carry (arithmetic) Feature extraction 02 engineering and technology Image (mathematics) 030507 speech-language pathology & audiology 03 medical and health sciences 0202 electrical engineering electronic engineering information engineering Key (cryptography) 020201 artificial intelligence & image processing Artificial intelligence 0305 other medical science business Image retrieval Similarity learning |
| Zdroj: | CVPR |
| DOI: | 10.1109/cvpr.2018.00804 |
| Popis: | In this paper, we investigate ways of conducting a detailed fashion search using query images and attributes. A credible fashion search platform should be able to (1) find images that share the same attributes as the query image, (2) allow users to manipulate certain attributes, e.g. replace collar attribute from round to v-neck, and (3) handle region-specific attribute manipulations, e.g. replacing the color attribute of the sleeve region without changing the color attribute of other regions. A key challenge to be addressed is that fashion products have multiple attributes and it is important for each of these attributes to have representative features. To address these challenges, we propose the FashionSearchNet which uses a weakly supervised localization method to extract regions of attributes. By doing so, unrelated features can be ignored thus improving the similarity learning. Also, FashionSearchNet incorporates a new procedure that enables region awareness to be able to handle region-specific requests. FashionSearchNet outperforms the most recent fashion search techniques and is shown to be able to carry out different search scenarios using the dynamic queries. |
| Databáze: | OpenAIRE |
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