Deep multi-view learning methods: A review
Multi-view learning (MVL) has attracted increasing attention and achieved great practical
success by exploiting complementary information of multiple features or modalities …
success by exploiting complementary information of multiple features or modalities …
Multimodal machine learning: A survey and taxonomy
Our experience of the world is multimodal-we see objects, hear sounds, feel texture, smell
odors, and taste flavors. Modality refers to the way in which something happens or is …
odors, and taste flavors. Modality refers to the way in which something happens or is …
A decade survey of content based image retrieval using deep learning
SR Dubey - IEEE Transactions on Circuits and Systems for …, 2021 - ieeexplore.ieee.org
The content based image retrieval aims to find the similar images from a large scale dataset
against a query image. Generally, the similarity between the representative features of the …
against a query image. Generally, the similarity between the representative features of the …
Fine-grained video-text retrieval with hierarchical graph reasoning
Cross-modal retrieval between videos and texts has attracted growing attentions due to the
rapid emergence of videos on the web. The current dominant approach is to learn a joint …
rapid emergence of videos on the web. The current dominant approach is to learn a joint …
Deep multimodal representation learning: A survey
W Guo, J Wang, S Wang - Ieee Access, 2019 - ieeexplore.ieee.org
Multimodal representation learning, which aims to narrow the heterogeneity gap among
different modalities, plays an indispensable role in the utilization of ubiquitous multimodal …
different modalities, plays an indispensable role in the utilization of ubiquitous multimodal …
Hit: Hierarchical transformer with momentum contrast for video-text retrieval
Abstract Video-Text Retrieval has been a hot research topic with the growth of multimedia
data on the internet. Transformer for video-text learning has attracted increasing attention …
data on the internet. Transformer for video-text learning has attracted increasing attention …
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 …
A survey of deep neural network architectures and their applications
Since the proposal of a fast learning algorithm for deep belief networks in 2006, the deep
learning techniques have drawn ever-increasing research interests because of their …
learning techniques have drawn ever-increasing research interests because of their …
Adversarial cross-modal retrieval
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 …
Multimodal co-learning: Challenges, applications with datasets, recent advances and future directions
Multimodal deep learning systems that employ multiple modalities like text, image, audio,
video, etc., are showing better performance than individual modalities (ie, unimodal) …
video, etc., are showing better performance than individual modalities (ie, unimodal) …