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 …

The purpose of cross-modal retrieval is to find the relationship between different modal
samples and to retrieve other modal samples with similar semantics by using a certain …

Cross-modal retrieval aims to retrieve relevant data from another modality when given a
query of one modality. Although most existing methods that rely on the label information 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, to provide flexible retrieval
experience across multimedia data such as image, video, text and audio. The core 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 …

The problem of cross-modal retrieval, eg, using a text query to search for images and vice-
versa, is considered in this paper. A novel model involving correspondence autoencoder …

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, 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 …

Recently, cross-modal retrieval is emerging with the help of deep multimodal learning.
However, even for unimodal data, collecting large-scale well-annotated data is expensive …