Title

Learning Cross-Modal Deep Embeddings for Multi-Object Image Retrieval using Text and Sketch

Document Type

Conference Article

Publication Title

Proceedings - International Conference on Pattern Recognition

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.

First Page

916

Last Page

921

DOI

10.1109/ICPR.2018.8545452

Publication Date

11-26-2018

Comments

Open Access, Green

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