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

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 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 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 …

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 has become a highlighted research topic, to provide flexible retrieval
experience across multimedia data such as image, video, text and audio. The core of …

Cross-media retrieval (CMR) offers a flexible retrieval experience across multiple modalities.
Existing CMR approaches are constrained by the assumption that the paired modalities are …

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 …

Cross-modal retrieval has received considerable attention owing to its applicability to enable
users to search desired information with diversified forms. Existing retrieval methods retain …

The cross-modal retrieval task has raised continuous attention in recent years with the
increasing scale of multi-modal data, which has broad application prospects including …