Cross-modal retrieval, eg, using an image query to search related text and vice-versa, has
become a highlighted research topic, to provide flexible retrieval experience across multi …

Cross-modal retrieval has drawn wide interest for retrieval across different modalities (such
as text, image, video, audio, and 3-D model). However, existing methods based on a deep …

Cross-modal retrieval aims to narrow the heterogeneity gap between different modalities,
such as retrieving images through texts or vice versa. One of the key challenges of cross …

With the explosive growth of multimedia data on the Internet, cross-modal retrieval has
attracted a great deal of attention in both computer vision and multimedia communities …

Cross-modal retrieval aims to enable flexible retrieval across different modalities. The core
of cross-modal retrieval is how to measure the content similarity between different types of …

Cross-modal retrieval aims to realize accurate and flexible retrieval across different
modalities of data, eg, image and text, which has achieved significant progress in recent …

Cross-modal retrieval aims at retrieving relevant points across different modalities, such as
retrieving images via texts. One key challenge of cross-modal retrieval is narrowing the …

Cross-modal retrieval (eg, query a given image to obtain a semantically similar sentence,
and vice versa) is an important but challenging task, as the heterogeneous gap and …

Cross-modal retrieval is an important multimedia research area which aims to take one type
of data as the query to retrieve relevant data of another type. Most of the existing methods …

Cross-modal retrieval has become a hot research topic in recent years for its theoretical and
practical significance. This paper proposes a new technique for learning such deep visual …