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A survey on deep learning for multimodal data fusion
With the wide deployments of heterogeneous networks, huge amounts of data with
characteristics of high volume, high variety, high velocity, and high veracity are generated …
characteristics of high volume, high variety, high velocity, and high veracity are generated …
An analytical study of information extraction from unstructured and multidimensional big data
Process of information extraction (IE) is used to extract useful information from unstructured
or semi-structured data. Big data arise new challenges for IE techniques with the rapid …
or semi-structured data. Big data arise new challenges for IE techniques with the rapid …
A survey on deep multimodal learning for computer vision: advances, trends, applications, and datasets
The research progress in multimodal learning has grown rapidly over the last decade in
several areas, especially in computer vision. The growing potential of multimodal data …
several areas, especially in computer vision. The growing potential of multimodal data …
Negative-aware attention framework for image-text matching
Image-text matching, as a fundamental task, bridges the gap between vision and language.
The key of this task is to accurately measure similarity between these two modalities. Prior …
The key of this task is to accurately measure similarity between these two modalities. Prior …
Multi-modal knowledge graph construction and application: A survey
Recent years have witnessed the resurgence of knowledge engineering which is featured
by the fast growth of knowledge graphs. However, most of existing knowledge graphs are …
by the fast growth of knowledge graphs. However, most of existing knowledge graphs are …
Tedigan: Text-guided diverse face image generation and manipulation
In this work, we propose TediGAN, a novel framework for multi-modal image generation and
manipulation with textual descriptions. The proposed method consists of three components …
manipulation with textual descriptions. The proposed method consists of three components …
Unicoder-vl: A universal encoder for vision and language by cross-modal pre-training
We propose Unicoder-VL, a universal encoder that aims to learn joint representations of
vision and language in a pre-training manner. Borrow ideas from cross-lingual pre-trained …
vision and language in a pre-training manner. Borrow ideas from cross-lingual pre-trained …
Imram: Iterative matching with recurrent attention memory for cross-modal image-text retrieval
Enabling bi-directional retrieval of images and texts is important for understanding the
correspondence between vision and language. Existing methods leverage the attention …
correspondence between vision and language. Existing methods leverage the attention …
Stock price prediction using deep learning and frequency decomposition
Nonlinearity and high volatility of financial time series have made it difficult to predict stock
price. However, thanks to recent developments in deep learning and methods such as long …
price. However, thanks to recent developments in deep learning and methods such as long …
Context-aware attention network for image-text retrieval
As a typical cross-modal problem, image-text bi-directional retrieval relies heavily on the
joint embedding learning and similarity measure for each image-text pair. It remains …
joint embedding learning and similarity measure for each image-text pair. It remains …