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In this work we introduce a cross modal image retrieval system that allowsboth text and sketch as input modalities for the query. A cross-modal deepnetwork architecture is formulated to jointly model the sketch and text inputmodalities as well as the the image output modality, learning a commonembedding between text and images and between sketches and images. In addition,an attention model is used to selectively focus the attention on the differentobjects of the image, allowing for retrieval with multiple objects in thequery. Experiments show that the proposed method performs the best in bothsingle and multiple object image retrieval in standard datasets.

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