An overview of cross-media retrieval: Concepts, methodologies, benchmarks, and challenges
Multimedia retrieval plays an indispensable role in big data utilization. Past efforts mainly
focused on single-media retrieval. However, the requirements of users are highly flexible …
focused on single-media retrieval. However, the requirements of users are highly flexible …
Comparative analysis on cross-modal information retrieval: A review
Human beings experience life through a spectrum of modes such as vision, taste, hearing,
smell, and touch. These multiple modes are integrated for information processing in our …
smell, and touch. These multiple modes are integrated for information processing in our …
Deep supervised cross-modal retrieval
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 …
of cross-modal retrieval is how to measure the content similarity between different types of …
Adversarial cross-modal retrieval
B Wang, Y Yang, X Xu, A Hanjalic… - Proceedings of the 25th …, 2017 - dl.acm.org
Cross-modal retrieval aims to enable flexible retrieval experience across different modalities
(eg, texts vs. images). The core of cross-modal retrieval research is to learn a common …
(eg, texts vs. images). The core of cross-modal retrieval research is to learn a common …
Hi-net: hybrid-fusion network for multi-modal MR image synthesis
Magnetic resonance imaging (MRI) is a widely used neuroimaging technique that can
provide images of different contrasts (ie, modalities). Fusing this multi-modal data has …
provide images of different contrasts (ie, modalities). Fusing this multi-modal data has …
[HTML][HTML] X-ModalNet: A semi-supervised deep cross-modal network for classification of remote sensing data
This paper addresses the problem of semi-supervised transfer learning with limited cross-
modality data in remote sensing. A large amount of multi-modal earth observation images …
modality data in remote sensing. A large amount of multi-modal earth observation images …
Survey on deep multi-modal data analytics: Collaboration, rivalry, and fusion
Y Wang - ACM Transactions on Multimedia Computing …, 2021 - dl.acm.org
With the development of web technology, multi-modal or multi-view data has surged as a
major stream for big data, where each modal/view encodes individual property of data …
major stream for big data, where each modal/view encodes individual property of data …
Ternary adversarial networks with self-supervision for zero-shot cross-modal retrieval
Given a query instance from one modality (eg, image), cross-modal retrieval aims to find
semantically similar instances from another modality (eg, text). To perform cross-modal …
semantically similar instances from another modality (eg, text). To perform cross-modal …
CM-GANs: Cross-modal generative adversarial networks for common representation learning
It is known that the inconsistent distributions and representations of different modalities, such
as image and text, cause the heterogeneity gap, which makes it very challenging to correlate …
as image and text, cause the heterogeneity gap, which makes it very challenging to correlate …
Deep adversarial metric learning for cross-modal retrieval
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 …
experience across multimedia data such as image, video, text and audio. The core of …