A Deep Local and Global Scene-Graph Matching for Image-Text Retrieval
نویسندگان
چکیده
Conventional approaches to image-text retrieval mainly focus on indexing visual objects appearing in pictures but ignore the interactions between these objects. Such occurrences and are equivalently useful important this field as they usually mentioned text. Scene graph presentation is a suitable method for matching challenge obtained good results due its ability capture inter-relationship information. Both images text represented scene levels formulate challenge. In paper, we introduce Local Global Graph Matching (LGSGM) model that enhances state-of-the-art by integrating an extra convolution network general information of graph. Specifically, pair graphs image caption, two separate models used learn features each graph’s nodes edges. Then Siamese-structure employed embed into vector forms. We finally combine graph-level vector-level calculate similarity pair. The empirical experiments show our enhancement with combination can improve performance baseline increasing recall more than 10% Flickr30k dataset. Our implementation code be found at https://github.com/m2man/LGSGM.
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ژورنال
عنوان ژورنال: Frontiers in artificial intelligence and applications
سال: 2021
ISSN: ['1879-8314', '0922-6389']
DOI: https://doi.org/10.3233/faia210049