maps text and images to a shared vector space

Review of Related Literature: Mapping Text and Images to a Shared Vector Space

The integration of textual and visual data has become increasingly important in the era of social media and advanced machine learning techniques. Several research efforts have focused on mapping text and images to a shared vector space to enhance semantic understanding and information retrieval. This review summarizes key findings from recent studies in this domain.

Semantic Correlation in Image-Text Weibo

One significant study explores the semantic correlation between images and text in the context of social media platforms like Sina Weibo. The researchers developed a model that extracts textual-linguistic, visual, and social features and projects them into a unified feature space using a genetic algorithm. A support vector machine (SVM) is then employed to recognize semantic correlations. The experimental results indicate that this approach significantly outperforms traditional SVM models that do not utilize feature space mapping.

Generative Models for Text-to-Image Mapping

Another notable contribution is a conditional generative model that maps low-dimensional embeddings of images and natural language to a common latent space. This model employs a constrained optimization procedure to project the embeddings onto a shared manifold, enabling the extraction of semantic relationships. The study introduces a proxy variable trick to facilitate independent conditional inference, ensuring that the separate latent spaces lie close to each other by minimizing the Euclidean distance between their distribution functions. The model demonstrates its effectiveness by generating specific colored images from text data, showcasing its potential for applications like double MNIST digits with color attributes.

Dynamic Binding of Text and Images

A different approach focuses on dynamically binding text and images to improve information communication. This study highlights the creation of a bi-directional linkage between the image space of a visualization program and hypertext space. By synchronizing dynamical image and text representations, the consistency of visual information and context is maintained. The researchers developed a simple mapping application using XML, HTML, and scalable vector graphics (SVG) to demonstrate these principles, emphasizing the historical relationship between text and image in art as a foundation for their work.

Conclusion

The reviewed studies collectively advance the field of mapping text and images to a shared vector space, each contributing unique methodologies and applications. From semantic correlation recognition in social media to generative models and dynamic binding for enhanced information communication, these research efforts underscore the importance and potential of integrating textual and visual data.

References

Recognizing semantic correlation in image-text weibo via feature space mapping.
Text to image generative model using constrained embedding space mapping.
Dynamically binding image to text for information communication.

Maps text and images to a shared vector space

Summary

Review of Related Literature: Mapping Text and Images to a Shared Vector Space

Semantic Correlation in Image-Text Weibo

Generative Models for Text-to-Image Mapping

Dynamic Binding of Text and Images

Conclusion

References

Sources and full results

Recognizing semantic correlation in image-text weibo via feature space mapping

Our semantic correlation recognition model using feature space mapping and support vector machine, using textual-linguistic, visual, and social features, significantly improves accuracy compared to traditional support vector machine models.

Text to image generative model using constrained embedding space mapping

This method enables the generation of specific colored images from text data by mapping low-dimensional embeddings of image and natural language to a common latent space, extracting semantic relationships between them.

Dynamically binding image to text for information communication

Dynamically binding image and text in interactive visualization programs improves information communication and consistency, as demonstrated in a simple mapping application using XML, HTML, and SVG.

Unpaired Image-to-Image Translation from Shared Deep Space

The proposed shared deep space generative adversarial network (SDSGAN) improves unpaired image-to-image translation by utilizing skip-connection block and self-reconstruction loss, outperforming existing methods.

Text-Guided Human Image Manipulation via Image-Text Shared Space

Our method improves human image manipulation accuracy and interactiveness by using structured information, disentangling appearance and spatial structure, and learning image-text shared space.

Vector Quantized Diffusion Model for Text-to-Image Synthesis

The VQ-Diffusion model for text-to-image generation improves image quality and speed, achieving a better trade-off between quality and speed compared to traditional autoregressive methods.

A robust arbitrary text detection system for natural scene images

Our robust system using Mutual Direction Symmetry, Mutual Magnitude Symmetry, and Gradient Vector Symmetry effectively detects text in natural scene images, regardless of orientation and curves.

Texture-Based Approach for Text Detection in Images Using Support Vector Machines and Continuously Adaptive Mean Shift Algorithm

This paper presents a novel texture-based method for text detection in images using support vector machines and the continuously adaptive mean shift algorithm (CAMSHIFT), resulting in robust and efficient results.

DRIT++: Diverse Image-to-Image Translation via Disentangled Representations

Our model can generate diverse and realistic images without paired training data, using content features and attribute vectors from a single input image.

MFS-Map: efficient context and content combination to annotate images

MFS-Map improves image annotation accuracy and speed by combining multiple content and context representations, achieving superior precision and faster speed compared to other methods.

Try another search