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Author (up) Sounak Dey; Anjan Dutta; Suman Ghosh; Ernest Valveny; Josep Llados; Umapada Pal edit   pdf
doi  openurl
  Title Learning Cross-Modal Deep Embeddings for Multi-Object Image Retrieval using Text and Sketch Type Conference Article
  Year 2018 Publication 24th International Conference on Pattern Recognition Abbreviated Journal  
  Volume Issue Pages 916 - 921  
  Abstract In this work we introduce a cross modal image retrieval system that allows both text and sketch as input modalities for the query. A cross-modal deep network architecture is formulated to jointly model the sketch and text input modalities as well as the the image output modality, learning a common embedding between text and images and between sketches and images. In addition, an attention model is used to selectively focus the attention on the different objects of the image, allowing for retrieval with multiple objects in the query. Experiments show that the proposed method performs the best in both single and multiple object image retrieval in standard datasets.  
  Address Beijing; China; August 2018  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference ICPR  
  Notes DAG; 602.167; 602.168; 600.097; 600.084; 600.121; 600.129 Approved no  
  Call Number Admin @ si @ DDG2018b Serial 3152  
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