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	Author	Sounak Dey; Anjan Dutta; Suman Ghosh; Ernest Valveny; Josep Llados
	Title	Aligning Salient Objects to Queries: A Multi-modal and Multi-object Image Retrieval Framework			Type	Conference Article
	Year	2018	Publication	14th Asian Conference on Computer Vision	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract	In this paper we propose an approach for multi-modal image retrieval in multi-labelled images. A multi-modal deep network architecture is formulated to jointly model sketches and text as input query modalities into a common embedding space, which is then further aligned with the image feature space. Our architecture also relies on a salient object detection through a supervised LSTM-based visual attention model learned from convolutional features. Both the alignment between the queries and the image and the supervision of the attention on the images are obtained by generalizing the Hungarian Algorithm using different loss functions. This permits encoding the object-based features and its alignment with the query irrespective of the availability of the co-occurrence of different objects in the training set. We validate the performance of our approach on standard single/multi-object datasets, showing state-of-the art performance in every dataset.
	Address	Perth; Australia; December 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	ACCV
	Notes	DAG; 600.097; 600.121; 600.129			Approved	no
	Call Number	Admin @ si @ DDG2018a			Serial	3151
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