Metaverse & Generative Artificial Intelligence Thoughts
What is Generative Artificial Intelligence?
A type of artificial intelligence model capable of generating new, original content. These models are typically based on deep learning technologies and can learn from input data to generate new data or text. They have achieved success in many fields such as image generation and natural language processing. In the metaverse, generative artificial intelligence can be used to create new virtual items, environments, characters, etc., enriching the content of the metaverse.
What is the Metaverse?
The metaverse is a virtual, fully interconnected world that includes the integration of technologies such as artificial intelligence, virtual reality, and augmented reality, allowing people to engage in various activities within it. The metaverse is a complex system that requires a significant amount of technology and resources to realize.
The Relationship Between Generative Artificial Intelligence and the Metaverse
Generative artificial intelligence can provide new content and creativity for the metaverse, making it more vibrant and interesting. At the same time, the metaverse can provide more data and scenarios for generative artificial intelligence, enabling it to learn and generate content better.
How to Promote the Realization of the Metaverse?
To promote the realization of the metaverse, various measures need to be taken, including technological research and development, investment support, and policy guidance. Among them, generative artificial intelligence can provide unique value to the metaverse and can promote its realization in the following ways:
Providing rich content and creativity, making the metaverse more vibrant and interesting;
Optimizing interaction and user experience in the metaverse to enhance user participation;
Promoting the commercialization and value creation of the metaverse, driving it towards sustainable development;
Strengthening the security and privacy protection of the metaverse to safeguard user rights.
Future Strategic Technologies
Learning features from data through machine learning methods to generate entirely new, original data that is similar to the training data rather than copying it.
It is expected that by 2025, data generated by generative artificial intelligence will account for 10% of all human data.
When generated data exceeds 80%, will humanity fully enter the metaverse?
Gartner predicts that in the coming years, generative models will become more intelligent, adaptive, multimodal, interpretable, controllable, and creative applications will see growth, becoming faster, more efficient, and more personalized.
Profound Transformation
Driving content development, visual art creation, digital twin, automatic programming, etc.
Providing AI intuition for scientific research, generative artificial intelligence refers to AI systems that can generate human-like creations (such as text, images, music, etc.). These systems use machine learning algorithms to create new data by learning patterns from large datasets.
Promoting the integration of virtual and real (efficiency enhancement, experience enhancement, spiritual enhancement).
Mathematical Principles
Learning a probability distribution p(x) means learning how to generate samples that conform to that distribution. Once learning is complete, we can generate new samples by sampling from that distribution or presenting samples through a function f(x).
Scientific Challenges
The solution space is huge (how to effectively search and generate subspaces); in high-dimensional spaces, the solution space is often vast, so how to effectively search and generate subspaces is an important issue. Common methods include greedy search, genetic algorithms, Monte Carlo methods, and model-based optimization.
Macroscopic consistency (how to predict long-term movement changes of targets and structures); major solutions include optical flow-based methods and deep learning-based methods.
Microscopic clarity (how to effectively approach multimodal distributions), the key lies in how to effectively approach multimodal distributions; currently, the main solutions include interpolation-based methods and deep learning-based methods.
Existing Technologies
Learning probability distributions aims to learn a probability distribution model that fits the given data. Generally, learning probability distributions can be achieved through explicit solving, approximate solving, and implicit solving methods.
Neural network rendering refers to using neural networks to synthesize high-quality images or videos. The core idea is to model the rendering problem as a function approximation problem, where input scene descriptions and parameters yield synthesized images or videos.
Technology Trends
From generation to inference (apparent simulation –> internal mechanism inference of physical phenomena), world models are closer to physical reality.
From flat to three-dimensional (stereoscopic visual rendering, multimodal driving, dynamic simulation), digital humans are more realistic and versatile.
Digital humans interact with world models (training agents on world models can feedback the decision-making process in the real world).
Complex Structure Modeling of Image Documents
Background
The structural information of different elements in the document is recognized by scanning the document, identifying titles and content (Chinese characters, tables).
Structured modeling based on encoder models.
Radical Modeling Based on Radicals
The joint optimization strategy design of the generative system involves multiple issues and technologies in the field of intelligent document processing, such as document structure modeling, typo detection, table detection, PDF parsing, neural network rendering, etc. These technologies can be jointly used to achieve various tasks in intelligent document processing, such as text recognition, table recognition, image recognition, document analysis, etc.
Attention visualization in recognition and generation tasks; attention mechanisms are widely used in recognition and generation tasks to allocate different parts of text information to corresponding modeling units.
The impact of out-of-set Chinese character generation on recognition performance; traditional Chinese character recognition systems typically train and test models based on known Chinese character sets, which are predetermined. If out-of-set Chinese characters appear in the test set, traditional recognition systems are likely to fail to recognize them correctly, as these characters are not in the training set. Therefore, the presence of out-of-set Chinese characters can severely affect the performance of recognition systems.
Performance analysis of the joint optimization strategy; first, the joint optimization strategy can enhance the model’s generalization ability, allowing it to perform well on new data; secondly, it can improve computational efficiency; finally, the convergence speed of the joint optimization strategy also needs to be analyzed.
Weakening the language model to improve the recall rate of typo recognition; due to the strong dependence of language models on prior knowledge and patterns of language, when the input data domain does not match the training domain of the language model, it may produce incorrect correction results. Therefore, weakening the influence of the language model to improve the recall rate of typo recognition is a feasible approach.
The principle of tree decoders; the basic principle is to transform the typo recognition problem into a sequence labeling problem by establishing a candidate set of typos and a correct dictionary to correct erroneous characters.
Decoding dependency relationships refer to the influence of previous predicted labels on the prediction of the current label in tasks such as sequence labeling.
Decoding algorithm flow and experimental results; decoding is an important step in natural language processing, aiming to obtain the optimal output sequence or structure based on the model’s predicted scores. In practical applications, depending on the task and the characteristics of the decoding algorithm, it is necessary to select an appropriate decoding algorithm. Additionally, for different tasks and models, the analysis of decoding dependency relationships and optimization of decoding algorithms can be performed to enhance model performance.
Visualization analysis of typo detection and error localization, where visualization analysis plays an important role in typo detection and error localization tasks, helping us better understand and analyze data and model results, thus improving task efficiency and accuracy.
Based on SEM Tables
split: Splitting table images into basic grids is an important preprocessing step in table recognition and understanding, aimed at dividing the table image into basic cells to provide a foundation for subsequent table structure analysis and content recognition.
Extracting grid-level multimodal features is a key issue in table recognition and understanding. The content in tables often includes multiple types such as text, images, and formulas, necessitating the use of different types of features to describe the content of cells for subsequent content recognition and structural analysis.
merge: Completing the merging of basic grids and predictions; in table images, each cell may consist of multiple basic grids, thus requiring the merging of adjacent basic grids into a single cell for subsequent content recognition and structural analysis.
Handling cross-row and cross-column table cells is a critical task in table recognition and understanding, involving cell merging and splitting, which significantly impacts table structure analysis and content recognition.
Handling multi-line text table cells primarily involves merging cross-row text into the same table cell for recognition and analysis; this requires careful consideration of semantic and layout information in the table to ensure that the merged table cell is readable and structurally sound. Additionally, various text types and styles may exist within the table cell, necessitating the comprehensive use of multiple features for cross-row text merging to enhance the accuracy and robustness of table recognition and understanding.
Based on Document Pre-training Models
Document structure: Text line-level tree visualization is a common representation of document structure, presenting the structural relationships at the text line level in a tree structure, facilitating user understanding and editing of documents.
Document structuring tasks involve transforming unstructured or semi-structured data within documents into structured data for subsequent processing and analysis.
PDF parsing systems + chart detection models can automate the parsing of charts within PDF documents, facilitating subsequent data analysis and processing.
Model setup: Decomposing overall tasks is a common modeling setup technique, breaking down a complex task into multiple simple sub-tasks and designing different models or model combinations for each sub-task, thus enhancing the overall model’s performance and interpretability.
Training setup: Joint learning is a method that utilizes multiple related tasks or multiple data sources for joint training. During training, the model simultaneously considers information from multiple tasks or data sources, thereby improving the model’s generalization ability and performance.
Results
Low-Level Visual Technology in Document Image Processing
The following document image processing technologies are key technologies of Hehe Information Company. Dr. Guo Fengjun, the R&D director of image algorithms at Hehe Information, shares the typical problems encountered by current low-level visual technologies when processing deformations, blurs, shadow occlusions, and chaotic backgrounds in documents, as well as the research achievements of the company’s technical team in intelligent image processing technology modules, typical applications of fusion technology, and image security fields. Hehe Information has been deeply engaged in core technology fields such as intelligent text recognition, image processing, natural language processing (NLP), knowledge graphs, and big data mining for over ten years, holding more than a hundred independent intellectual property invention patents.
Intelligent Document Scanning
ROI Extraction
Receipt ROI extraction
Multi-business card ROI extraction
Deformation Correction
Deformation correction is an important preprocessing step in image recognition, aiming to correct the input image so that its shape, size, orientation, etc., match the template image, thus enhancing the accuracy and stability of subsequent recognition models.
Document Restoration
Correction networks, based on correction networks, implement deformation correction by training a correction network. Such methods typically use convolutional neural networks (CNNs) or recurrent neural networks (RNNs) to map the input image to a shape similar to the template image. This method does not require feature point matching, thus offering high computational efficiency and stability but requires a large amount of training data and model tuning, and accuracy is influenced by model design and training data.
Result Evaluation
Image Recovery – Shadow Removal
Quality Enhancement
Intelligent HD uses super-resolution and other techniques to increase the resolution and clarity of images, typically achieved through machine learning algorithms.
Removing moiré patterns; moiré patterns are a common interference in digital images, which can be removed using image processing methods. One method for removing moiré patterns is to perform a wavelet transform on the image, remove low-frequency components, and adjust high-frequency components.
Moiré removal effect
A handwriting erasure framework is a method used in handwriting character recognition; by introducing learnable erasure operations into the neural network, it can mitigate the impact of data noise on recognition performance.
Handwriting erasure effect
Image Tampering Detection
Photoshop Tampering Detection
Traditional Exif-based Photoshop Detection
This method checks the Exif information of images to determine whether they have been edited using tools like Photoshop.
Network Structure
Photoshop Tampering Detection Experience
More Functionality Experience Address
Summary
Generative artificial intelligence is a deep learning-based AI technology that can generate new data, images, language, and other content by learning patterns and rules from massive datasets. This technology can bring significant commercial value across various industries.
As an ordinary person, how can we seize the wave of technological change? We can start from the following four points:
Stay informed about relevant news and developments: Keep an eye on the latest news and developments in the field of artificial intelligence to understand the latest technological advancements and application scenarios, which helps better grasp the development trends and future application directions of artificial intelligence.
Learn relevant knowledge and skills: Learning related knowledge and skills such as machine learning, deep learning, programming, etc., helps understand the basic principles and implementation methods of artificial intelligence, preparing for future development.
Participate in related communities and activities: Join relevant AI communities and participate in related activities to communicate with other enthusiasts and professionals, share experiences and viewpoints, expand one’s perspective and network, and learn more information and opportunities.
Innovation and practice: Try using existing technologies and tools for innovation and practice, such as attempting to use generative artificial intelligence technology to generate interesting images, music, or text. This helps improve one’s skill level and creativity while accumulating experience for future development.
Seizing the wave of generative artificial intelligence requires continuous learning, practice, and innovation, as well as maintaining an open mind and positive attitude, keeping pace with the latest developments and application scenarios of artificial intelligence, laying a solid foundation for future development.