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 …

DNN-based cross-modal retrieval is a research hotspot to retrieve across different modalities
as image and text, but existing methods often face the challenge of insufficient cross-modal …

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

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 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 takes one type of data as the query to retrieve relevant data of another
type. Most of existing cross-modal retrieval approaches were proposed to learn a common …

Cross-modal retrieval has become a highlighted research topic for retrieval across
multimedia data such as image and text. A two-stage learning framework is widely adopted …

The rapid development of Deep Neural Networks (DNNs) in single-modal retrieval has
promoted the wide application of DNNs in cross-modal retrieval tasks. Therefore, we …

Cross-modal retrieval (CMR) enables flexible retrieval experience across different
modalities (eg, texts versus images), which maximally benefits us from the abundance of …