Generative AI has become the buzzword of 2023. Whether text-generating ChatGPT or
image-generating Midjourney, generative AI tools have transformed businesses and
dominated the content creation industry.
How to prepare a perfect video abstract for your research paper – Pubrica.pdfPubrica
A video abstract is a series of moving pictures taken from a lengthier movie that is significantly shorter than the original yet retains the original's essential meaning.
Learn More : https://bit.ly/3JVyrCW
Reference: https://pubrica.com/services/publication-support/Video-Abstract/
Why Pubrica:
When you order our services, we promise you the following – Plagiarism free | always on Time | 24*7 customer support | Written to international Standard | Unlimited Revisions support | Medical writing Expert | Publication Support | Bio statistical experts | High-quality Subject Matter Experts.
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Here are Important things about Image and Video Annotation that you should know for machine learning and to make your annotation project well & good your vision our thoughts.
Generative AI models, such as GANs and VAEs, have the potential to create realistic and diverse synthetic data for various applications, from image and speech synthesis to drug discovery and language modeling. However, training these models can be challenging due to the instability and mode collapse issues that often arise. In this workshop, we will explore how stable diffusion, a recent training method that combines diffusion models and Langevin dynamics, can address these challenges and improve the performance and stability of generative models. We will use a pre-configured development environment for machine learning, to run hands-on experiments and train stable diffusion models on different datasets. By the end of the session, attendees will have a better understanding of generative AI and stable diffusion, and how to build and deploy stable generative models for real-world use cases.
How to prepare a perfect video abstract for your research paper – Pubrica.pdfPubrica
A video abstract is a series of moving pictures taken from a lengthier movie that is significantly shorter than the original yet retains the original's essential meaning.
Learn More : https://bit.ly/3JVyrCW
Reference: https://pubrica.com/services/publication-support/Video-Abstract/
Why Pubrica:
When you order our services, we promise you the following – Plagiarism free | always on Time | 24*7 customer support | Written to international Standard | Unlimited Revisions support | Medical writing Expert | Publication Support | Bio statistical experts | High-quality Subject Matter Experts.
Contact us:
Web: https://pubrica.com/
Blog: https://pubrica.com/academy/
Email: sales@pubrica.com
WhatsApp : +91 9884350006
United Kingdom: +44-1618186353
Here are Important things about Image and Video Annotation that you should know for machine learning and to make your annotation project well & good your vision our thoughts.
Generative AI models, such as GANs and VAEs, have the potential to create realistic and diverse synthetic data for various applications, from image and speech synthesis to drug discovery and language modeling. However, training these models can be challenging due to the instability and mode collapse issues that often arise. In this workshop, we will explore how stable diffusion, a recent training method that combines diffusion models and Langevin dynamics, can address these challenges and improve the performance and stability of generative models. We will use a pre-configured development environment for machine learning, to run hands-on experiments and train stable diffusion models on different datasets. By the end of the session, attendees will have a better understanding of generative AI and stable diffusion, and how to build and deploy stable generative models for real-world use cases.
Automatic semantic content extraction in videos using a fuzzy ontology and ru...IEEEFINALYEARPROJECTS
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09849539085, 09966235788 or mail us - ieeefinalsemprojects@gmail.co¬m-Visit Our Website: www.finalyearprojects.org
The development of AI/ML has given video stitching and its applications an entirely new dimension. Let’s examine the evolution of video stitching combined with AI/ML throughout time, the main difficulties developers have had to overcome, and the numerous uses for video stitching that use the newest AI-infused technology.
To know more about Video Stitching + AL/ML- Changing Landscape over the years, see https://www.logic-fruit.com/blog/al-ml/video-stitching-ai-ml/
About Logic Fruit Technologies
Logic Fruit Technologies is a product engineering R&D & consulting services provider for embedded systems and application development. We provide end-to-end solutions from the conception of the idea and design to the finished product. We have been servicing customers globally for over a decade.
The company has specific experience in various fields, such as
FPGA Design & hardware design
RTL IP Design
A variety of digital protocols
Communication buses as1G, 10G Ethernet
PCIe
DIGRF
STM16/64
HDMI.
Logic Fruit Technologies is also an expert in developing,
software-defined radio (SDR) IPs
Encryption
Signal generation
Data analysis, and
Multiple Image Processing Techniques.
Recently Logic Fruit technologies are also exploring FPGA acceleration on data centers for real-time data processing.
**Our Social Media Channels**
Facebook: https://www.facebook.com/LogicFruit/
Twitter: https://twitter.com/logicfruit
LinkedIn: https://www.linkedin.com/company/logi…
Website: https://www.logic-fruit.com/
#LFT #LogicFruitTechnologies #LogicFruit
Interested to view more SlideShares, Click on the below links,
https://www.slideshare.net/LogicFruit/a-designers-practical-guide-to-arinc-429-standard-3pptx
https://www.slideshare.net/LogicFruit/a-swift-introduction-to-milstd
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https://www.slideshare.net/LogicFruit/afdx
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https://www.slideshare.net/LogicFruit/afdx-a-timedeterministic-application-of-arinc-664-part-7
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How to prepare a perfect video abstract for your research paper – Pubrica.pptxPubrica
A video abstract is a series of moving pictures taken from a lengthier movie that is significantly shorter than the original yet retains the original's essential meaning.
Learn More : https://bit.ly/3JVyrCW
Reference: https://pubrica.com/services/publication-support/Video-Abstract/
Why Pubrica:
When you order our services, we promise you the following – Plagiarism free | always on Time | 24*7 customer support | Written to international Standard | Unlimited Revisions support | Medical writing Expert | Publication Support | Bio statistical experts | High-quality Subject Matter Experts.
Contact us:
Web: https://pubrica.com/
Blog: https://pubrica.com/academy/
Email: sales@pubrica.com
WhatsApp : +91 9884350006
United Kingdom: +44-1618186353
Generative AI models, such as ChatGPT and Stable Diffusion, can create new and original content like text, images, video, audio, or other data from simple prompts, as well as handle complex dialogs and reason about problems with or without images. These models are disrupting traditional technologies, from search and content creation to automation and problem solving, and are fundamentally shaping the future user interface to computing devices. Generative AI can apply broadly across industries, providing significant enhancements for utility, productivity, and entertainment. As generative AI adoption grows at record-setting speeds and computing demands increase, on-device and hybrid processing are more important than ever. Just like traditional computing evolved from mainframes to today’s mix of cloud and edge devices, AI processing will be distributed between them for AI to scale and reach its full potential.
In this presentation you’ll learn about:
- Why on-device AI is key
- Full-stack AI optimizations to make on-device AI possible and efficient
- Advanced techniques like quantization, distillation, and speculative decoding
- How generative AI models can be run on device and examples of some running now
- Qualcomm Technologies’ role in scaling on-device generative AI
The Importance of Video in Today's Digital Landscape.pdfCyrana Video
Collaborative Video Making Platform: Ideal for teams, the platform supports real-time collaboration and cloud-based storage, resulting in optimized workflows. Teams can concurrently script, edit, and finalize, speeding up production timelines and improving team productivity.
Video content analysis and retrieval system using video storytelling and inde...IJECEIAES
Videos are used often for communicating ideas, concepts, experience, and situations, because of the significant advances made in video communication technology. The social media platforms enhanced the video usage expeditiously. At, present, recognition of a video is done, using the metadata like video title, video descriptions, and video thumbnails. There are situations like video searcher requires only a video clip on a specific topic from a long video. This paper proposes a novel methodology for the analysis of video content and using video storytelling and indexing techniques for the retrieval of the intended video clip from a long duration video. Video storytelling technique is used for video content analysis and to produce a description of the video. The video description thus created is used for preparation of an index using wormhole algorithm, guarantying the search of a keyword of definite length L, within the minimum worst-case time. This video index can be used by video searching algorithm to retrieve the relevant part of the video by virtue of the frequency of the word in the keyword search of the video index. Instead of downloading and transferring a whole video, the user can download or transfer the specifically necessary video clip. The network constraints associated with the transfer of videos are considerably addressed.
Generative AI 101 A Beginners Guide.pdfSoluLab1231
Generative AI has emerged as a transformative technology in recent years, revolutionizing various industries with its potential to create original content such as images, text, and even music. The advancements in generative AI have enabled machines to learn, create and produce new content, leading to unprecedented innovation across various sectors. As a result, many companies are now considering generative AI technology and hiring Generative AI Development Companies to leverage its benefits and enhance their operations with AI-led automation.
Generative AI is the new future AI that focuses on learning, analyzing, and producing original content through machine learning algorithms. This technology is transforming businesses’ operations and enhancing their ability to provide customized solutions. It has become a hot topic in the market, with many companies investing in this technology to leverage its benefits.
Inverted File Based Search Technique for Video Copy Retrievalijcsa
A video copy detection system is a content-based search engine focusing on Spatio-temporal features. It
aims to find whether a query video segment is a copy of video from the video database or not based on the
signature of the video. It is hard to find whether a video is a copied video or a similar video since the
features of the content are very similar from one video to the other. The main focus is to detect that the
query video is present in the video database with robustness depending on the content of video and also by
fast search of fingerprints. The Fingerprint Extraction Algorithm and Fast Search Algorithm are adopted
to achieve robust, fast, efficient and accurate video copy detection. As a first step, the Fingerprint
Extraction algorithm is employed which extracts a fingerprint through the features from the image content
of video. The images are represented as Temporally Informative Representative Images (TIRI). Then the
next step is to find the presence of copy of a query video in a video database, in which a close match of its
fingerprint in the corresponding fingerprint database is searched using inverted-file-based method.
First to Market, World's First App Fully Powered by Google's New AI Technolog...DulalChandraMondal
AdvertAI Review: What is AdvertAi?
World's First App Fully Powered by Google's New AI Technology - Adanet & TensorFlow, Crafting Real-Time AI Ads Instantly. Advert AI generates Ad Copies, Ad Creatives, Advertisement Visuals, and Videos.
"AdvertAi" being the "World's First App Fully Powered by Google's New AI Technology - Adanet & TensorFlow, Crafting Real-Time AI Ads Instantly." It's possible that this is a development that has occurred after my last update or it could be a product or service that hasn't gained significant visibility up until that point.
"This app's capabilities are truly unbelievable! With just a handful of voice commands, I transformed my concepts into captivating Ad Copies and Stunning Ad images and videos.
The AI technology it employs is undeniably remarkable, substantially reducing the time and energy I invest."-Henry J.
More Besides Sora: Tools to Create Dynamic Videos from Textual ContentRachelWang856621
Undoubtedly, video content is more attractive than text. The demand for engaging visual content continues to soar quickly. As businesses and content creators strive to capture the attention of their audiences, tools that can seamlessly transform text into dynamic videos have become focus hots. So comes out OpenAI Sora. This text-to-video generative AI model looks incredibly impressive so far, introducing some huge potential across many industries.
Content based video retrieval using discrete cosine transformnooriasukmaningtyas
A content based video retrieval (CBVR) framework is built in this paper.
One of the essential features of video retrieval process and CBVR is a color
value. The discrete cosine transform (DCT) is used to extract a query video
features to compare with the video features stored in our database. Average
result of 0.6475 was obtained by using the DCT after implementing it to the
database we created and collected, and on all categories. This technique was
applied on our database of video, check 100 database videos, 5 videos in
Keywords: each category.
Drones generate vast amounts of data, which is usually in the form of images or video streams. Identification of objects of interest, counting them, or detecting change over time, are some of the tasks that are monotonous and labor intensive.
FlytBase AI platform offers a complete solution to automate such tasks. It has been designed and optimised specifically for drone applications.
Video content has dominated communication in the current digital era, grabbing audiences' attention and effectively delivering messages better than any other media. The creation of videos has been revolutionized by the AI Video Builder, a ground-breaking tool made possible by the quick development of artificial intelligence (AI). With its innovative features, including automatic video creation with customizable templates, automatic video editing, voice over synthesis, text to video conversion, and many more, the game-changing AI Video Builder has advanced traditional video creation. This makes creating videos one of the simplest things on earth.
The journal publishes original works with practical significance and academic value. All papers submitted to IJMRR are subject to a double-blind Authors are invited to submit theoretical or empirical papers in all aspects of management, including strategy, human resources, marketing, operations, technology, information systems, finance and accounting, business economics, public sector management. peer review process. IJMRR is an international forum for research that advances the theory and practice of management.
leewayhertz.com-Generative AI for enterprises The architecture its implementa...robertsamuel23
Businesses across industries are increasingly turning their attention to Generative AI
(GenAI) due to its vast potential for streamlining and optimizing operations.
leewayhertz.com-What role do embeddings play in a ChatGPT-like model.pdfrobertsamuel23
Machine learning is a subset of artificial intelligence that enables computers to learn from data and improve their performance over time without being explicitly programmed.
More Related Content
Similar to leewayhertz.com-How to create a Generative video model.pdf
Automatic semantic content extraction in videos using a fuzzy ontology and ru...IEEEFINALYEARPROJECTS
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09849539085, 09966235788 or mail us - ieeefinalsemprojects@gmail.co¬m-Visit Our Website: www.finalyearprojects.org
The development of AI/ML has given video stitching and its applications an entirely new dimension. Let’s examine the evolution of video stitching combined with AI/ML throughout time, the main difficulties developers have had to overcome, and the numerous uses for video stitching that use the newest AI-infused technology.
To know more about Video Stitching + AL/ML- Changing Landscape over the years, see https://www.logic-fruit.com/blog/al-ml/video-stitching-ai-ml/
About Logic Fruit Technologies
Logic Fruit Technologies is a product engineering R&D & consulting services provider for embedded systems and application development. We provide end-to-end solutions from the conception of the idea and design to the finished product. We have been servicing customers globally for over a decade.
The company has specific experience in various fields, such as
FPGA Design & hardware design
RTL IP Design
A variety of digital protocols
Communication buses as1G, 10G Ethernet
PCIe
DIGRF
STM16/64
HDMI.
Logic Fruit Technologies is also an expert in developing,
software-defined radio (SDR) IPs
Encryption
Signal generation
Data analysis, and
Multiple Image Processing Techniques.
Recently Logic Fruit technologies are also exploring FPGA acceleration on data centers for real-time data processing.
**Our Social Media Channels**
Facebook: https://www.facebook.com/LogicFruit/
Twitter: https://twitter.com/logicfruit
LinkedIn: https://www.linkedin.com/company/logi…
Website: https://www.logic-fruit.com/
#LFT #LogicFruitTechnologies #LogicFruit
Interested to view more SlideShares, Click on the below links,
https://www.slideshare.net/LogicFruit/a-designers-practical-guide-to-arinc-429-standard-3pptx
https://www.slideshare.net/LogicFruit/a-swift-introduction-to-milstd
https://www.slideshare.net/LogicFruit/arinc-the-ultimate-guide-to-modern-avionics-protocol/LogicFruit/arinc-the-ultimate-guide-to-modern-avionics-protocol
https://www.slideshare.net/LogicFruit/arinc-629-digital-data-bus-specifications/LogicFruit/arinc-629-digital-data-bus-specifications
https://www.slideshare.net/LogicFruit/afdx
https://www.slideshare.net/LogicFruit/end-system-design-parameters-of-the-arinc-664-part-7
https://www.slideshare.net/LogicFruit/compute-express-link-cxl-everything-you-ought-to-know
https://www.logic-fruit.com/blog/fpga/what-is-fpga/
https://www.slideshare.net/LogicFruit/cxl-vs-pcie-gen-5-the-brief-comparison
https://www.slideshare.net/LogicFruit/fpga-technology-development-and-market-trends-in-the-new-decade
https://www.slideshare.net/LogicFruit/fpga-design-an-ultimate-guide-for-fpga-enthusiasts
https://www.slideshare.net/LogicFruit/fpga-vs-asic-design-comparison
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https://www.slideshare.net/LogicFruit/fpgas-expansion-in-adas-autonomous-driving
https://www.slideshare.net/LogicFruit/take-a-step-ahead-with-an-upgrade-to-arinc-818-revision-3-avionic
How to prepare a perfect video abstract for your research paper – Pubrica.pptxPubrica
A video abstract is a series of moving pictures taken from a lengthier movie that is significantly shorter than the original yet retains the original's essential meaning.
Learn More : https://bit.ly/3JVyrCW
Reference: https://pubrica.com/services/publication-support/Video-Abstract/
Why Pubrica:
When you order our services, we promise you the following – Plagiarism free | always on Time | 24*7 customer support | Written to international Standard | Unlimited Revisions support | Medical writing Expert | Publication Support | Bio statistical experts | High-quality Subject Matter Experts.
Contact us:
Web: https://pubrica.com/
Blog: https://pubrica.com/academy/
Email: sales@pubrica.com
WhatsApp : +91 9884350006
United Kingdom: +44-1618186353
Generative AI models, such as ChatGPT and Stable Diffusion, can create new and original content like text, images, video, audio, or other data from simple prompts, as well as handle complex dialogs and reason about problems with or without images. These models are disrupting traditional technologies, from search and content creation to automation and problem solving, and are fundamentally shaping the future user interface to computing devices. Generative AI can apply broadly across industries, providing significant enhancements for utility, productivity, and entertainment. As generative AI adoption grows at record-setting speeds and computing demands increase, on-device and hybrid processing are more important than ever. Just like traditional computing evolved from mainframes to today’s mix of cloud and edge devices, AI processing will be distributed between them for AI to scale and reach its full potential.
In this presentation you’ll learn about:
- Why on-device AI is key
- Full-stack AI optimizations to make on-device AI possible and efficient
- Advanced techniques like quantization, distillation, and speculative decoding
- How generative AI models can be run on device and examples of some running now
- Qualcomm Technologies’ role in scaling on-device generative AI
The Importance of Video in Today's Digital Landscape.pdfCyrana Video
Collaborative Video Making Platform: Ideal for teams, the platform supports real-time collaboration and cloud-based storage, resulting in optimized workflows. Teams can concurrently script, edit, and finalize, speeding up production timelines and improving team productivity.
Video content analysis and retrieval system using video storytelling and inde...IJECEIAES
Videos are used often for communicating ideas, concepts, experience, and situations, because of the significant advances made in video communication technology. The social media platforms enhanced the video usage expeditiously. At, present, recognition of a video is done, using the metadata like video title, video descriptions, and video thumbnails. There are situations like video searcher requires only a video clip on a specific topic from a long video. This paper proposes a novel methodology for the analysis of video content and using video storytelling and indexing techniques for the retrieval of the intended video clip from a long duration video. Video storytelling technique is used for video content analysis and to produce a description of the video. The video description thus created is used for preparation of an index using wormhole algorithm, guarantying the search of a keyword of definite length L, within the minimum worst-case time. This video index can be used by video searching algorithm to retrieve the relevant part of the video by virtue of the frequency of the word in the keyword search of the video index. Instead of downloading and transferring a whole video, the user can download or transfer the specifically necessary video clip. The network constraints associated with the transfer of videos are considerably addressed.
Generative AI 101 A Beginners Guide.pdfSoluLab1231
Generative AI has emerged as a transformative technology in recent years, revolutionizing various industries with its potential to create original content such as images, text, and even music. The advancements in generative AI have enabled machines to learn, create and produce new content, leading to unprecedented innovation across various sectors. As a result, many companies are now considering generative AI technology and hiring Generative AI Development Companies to leverage its benefits and enhance their operations with AI-led automation.
Generative AI is the new future AI that focuses on learning, analyzing, and producing original content through machine learning algorithms. This technology is transforming businesses’ operations and enhancing their ability to provide customized solutions. It has become a hot topic in the market, with many companies investing in this technology to leverage its benefits.
Inverted File Based Search Technique for Video Copy Retrievalijcsa
A video copy detection system is a content-based search engine focusing on Spatio-temporal features. It
aims to find whether a query video segment is a copy of video from the video database or not based on the
signature of the video. It is hard to find whether a video is a copied video or a similar video since the
features of the content are very similar from one video to the other. The main focus is to detect that the
query video is present in the video database with robustness depending on the content of video and also by
fast search of fingerprints. The Fingerprint Extraction Algorithm and Fast Search Algorithm are adopted
to achieve robust, fast, efficient and accurate video copy detection. As a first step, the Fingerprint
Extraction algorithm is employed which extracts a fingerprint through the features from the image content
of video. The images are represented as Temporally Informative Representative Images (TIRI). Then the
next step is to find the presence of copy of a query video in a video database, in which a close match of its
fingerprint in the corresponding fingerprint database is searched using inverted-file-based method.
First to Market, World's First App Fully Powered by Google's New AI Technolog...DulalChandraMondal
AdvertAI Review: What is AdvertAi?
World's First App Fully Powered by Google's New AI Technology - Adanet & TensorFlow, Crafting Real-Time AI Ads Instantly. Advert AI generates Ad Copies, Ad Creatives, Advertisement Visuals, and Videos.
"AdvertAi" being the "World's First App Fully Powered by Google's New AI Technology - Adanet & TensorFlow, Crafting Real-Time AI Ads Instantly." It's possible that this is a development that has occurred after my last update or it could be a product or service that hasn't gained significant visibility up until that point.
"This app's capabilities are truly unbelievable! With just a handful of voice commands, I transformed my concepts into captivating Ad Copies and Stunning Ad images and videos.
The AI technology it employs is undeniably remarkable, substantially reducing the time and energy I invest."-Henry J.
More Besides Sora: Tools to Create Dynamic Videos from Textual ContentRachelWang856621
Undoubtedly, video content is more attractive than text. The demand for engaging visual content continues to soar quickly. As businesses and content creators strive to capture the attention of their audiences, tools that can seamlessly transform text into dynamic videos have become focus hots. So comes out OpenAI Sora. This text-to-video generative AI model looks incredibly impressive so far, introducing some huge potential across many industries.
Content based video retrieval using discrete cosine transformnooriasukmaningtyas
A content based video retrieval (CBVR) framework is built in this paper.
One of the essential features of video retrieval process and CBVR is a color
value. The discrete cosine transform (DCT) is used to extract a query video
features to compare with the video features stored in our database. Average
result of 0.6475 was obtained by using the DCT after implementing it to the
database we created and collected, and on all categories. This technique was
applied on our database of video, check 100 database videos, 5 videos in
Keywords: each category.
Drones generate vast amounts of data, which is usually in the form of images or video streams. Identification of objects of interest, counting them, or detecting change over time, are some of the tasks that are monotonous and labor intensive.
FlytBase AI platform offers a complete solution to automate such tasks. It has been designed and optimised specifically for drone applications.
Video content has dominated communication in the current digital era, grabbing audiences' attention and effectively delivering messages better than any other media. The creation of videos has been revolutionized by the AI Video Builder, a ground-breaking tool made possible by the quick development of artificial intelligence (AI). With its innovative features, including automatic video creation with customizable templates, automatic video editing, voice over synthesis, text to video conversion, and many more, the game-changing AI Video Builder has advanced traditional video creation. This makes creating videos one of the simplest things on earth.
The journal publishes original works with practical significance and academic value. All papers submitted to IJMRR are subject to a double-blind Authors are invited to submit theoretical or empirical papers in all aspects of management, including strategy, human resources, marketing, operations, technology, information systems, finance and accounting, business economics, public sector management. peer review process. IJMRR is an international forum for research that advances the theory and practice of management.
Similar to leewayhertz.com-How to create a Generative video model.pdf (20)
leewayhertz.com-Generative AI for enterprises The architecture its implementa...robertsamuel23
Businesses across industries are increasingly turning their attention to Generative AI
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Machine learning is a subset of artificial intelligence that enables computers to learn from data and improve their performance over time without being explicitly programmed.
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Artificial intelligence has made great strides in the area of content generation.
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At its core, generative artificial intelligence relies on the concept of generative models, which serve as engines that churn out entirely new data resembling their training data. It is like a sculptor who has studied so many forms found in nature and then uses this knowledge to create sculptures from his imagination that have never been seen before anywhere else. If taken to cyberspace, gans work almost the same way.
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[Note: This is a partial preview. To download this presentation, visit:
https://www.oeconsulting.com.sg/training-presentations]
Sustainability has become an increasingly critical topic as the world recognizes the need to protect our planet and its resources for future generations. Sustainability means meeting our current needs without compromising the ability of future generations to meet theirs. It involves long-term planning and consideration of the consequences of our actions. The goal is to create strategies that ensure the long-term viability of People, Planet, and Profit.
Leading companies such as Nike, Toyota, and Siemens are prioritizing sustainable innovation in their business models, setting an example for others to follow. In this Sustainability training presentation, you will learn key concepts, principles, and practices of sustainability applicable across industries. This training aims to create awareness and educate employees, senior executives, consultants, and other key stakeholders, including investors, policymakers, and supply chain partners, on the importance and implementation of sustainability.
LEARNING OBJECTIVES
1. Develop a comprehensive understanding of the fundamental principles and concepts that form the foundation of sustainability within corporate environments.
2. Explore the sustainability implementation model, focusing on effective measures and reporting strategies to track and communicate sustainability efforts.
3. Identify and define best practices and critical success factors essential for achieving sustainability goals within organizations.
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1. Introduction and Key Concepts of Sustainability
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3. Measures and Reporting in Sustainability
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To download the complete presentation, visit: https://www.oeconsulting.com.sg/training-presentations
The world of search engine optimization (SEO) is buzzing with discussions after Google confirmed that around 2,500 leaked internal documents related to its Search feature are indeed authentic. The revelation has sparked significant concerns within the SEO community. The leaked documents were initially reported by SEO experts Rand Fishkin and Mike King, igniting widespread analysis and discourse. For More Info:- https://news.arihantwebtech.com/search-disrupted-googles-leaked-documents-rock-the-seo-world/
Digital Transformation and IT Strategy Toolkit and TemplatesAurelien Domont, MBA
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Editable Toolkit to help you reuse our content: 700 Powerpoint slides | 35 Excel sheets | 84 minutes of Video training
This PowerPoint presentation is only a small preview of our Toolkits. For more details, visit www.domontconsulting.com
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Dr. William Harvey is a seasoned Operations Leader with extensive experience in chemical processing, manufacturing, and operations management. At Michelman, he currently oversees multiple sites, leading teams in strategic planning and coaching/practicing continuous improvement. William is set to start his eighth year of teaching at the University of Cincinnati where he teaches marketing, finance, and management. William holds various certifications in change management, quality, leadership, operational excellence, team building, and DiSC, among others.
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leewayhertz.com-How to create a Generative video model.pdf
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How to create a Generative video model?
leewayhertz.com/create-generative-video-model
Generative AI has become the buzzword of 2023. Whether text-generating ChatGPT or
image-generating Midjourney, generative AI tools have transformed businesses and
dominated the content creation industry. With Microsoft’s partnership with OpenAI and
Google creating its own AI-powered chatbot called Bard, it is fast growing into one of the
hottest areas within the tech sphere.
Generative AI aims to generate new data similar to the training dataset. It utilizes
machine learning algorithms called generative models to learn the patterns and
distributions underlying the training data. Although different generative models are
available that produce text, images, audio, codes and videos, this article will take a deep
dive into generative video models.
From generating video using text descriptions to generating new scenes and characters
and enhancing the quality of a video, generative video models offer a wealth of
opportunities for video content creators. Generative video platforms are often powered by
sophisticated models like GANs, VAEs, or CGANs, capable of translating human language
to build images and videos. In this article, you will learn about generative video models,
their advantages, and how they work, followed by a step-by-step guide on creating your
own generative video model
Generative models and their types
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Generative models create new data similar to the training data using machine learning
algorithms. To create new data, these models undergo a series of training wherein they
are exposed to large datasets. They learn the underlying patterns and relationships in the
training data to produce similar synthetic data based on their knowledge acquired from
the training. Once trained, these models take text prompts (sometimes image prompts) to
generate content based on the text.
There are several different types of generative models, including:
1. Generative Adversarial Networks (GANs): GANs are based on a two-part model,
where one part, called the generator, generates fake data, and the other, the
discriminator, evaluates the fake data’s authenticity. The generator’s goal is to
produce fake data that is so convincing that the discriminator cannot tell the
difference between fake and real data.
2. Stable Diffusion Models (SDMs): SDMs, also known as Flow-based Generative
Models, transform a simple random noise into more complex and structured data,
like an image or a video. They do this by defining a series of simple transformations,
called flows, that gradually change the random noise into the desired data.
3. Autoregressive Models: Autoregressive models generate data one piece at a time,
such as generating one word in a sentence at a time. They do this by predicting the
next piece of data based on the previous pieces.
4. Variational Autoencoders (VAEs): VAEs work by encoding the training data into a
lower-dimensional representation, known as a latent code, and then decoding the
latent code back into the original data space to generate new data. The goal is to find
the best latent code to generate data similar to the original data.
5. Convolutional Generative Adversarial Networks (CGANs): CGANs are a type of GAN
specifically designed for image and video data. They use convolutional neural
networks to learn the relationships between the different parts of an image or video,
making them well-suited for tasks like video synthesis.
These are some of the most typically used generative models, but many others have been
developed for specific use cases. The choice of which model to use will depend on the
specific requirements of the task at hand.
What is a generative video model?
Generative video models are machine learning algorithms that generate new video data
based on patterns and relationships learned from training datasets. In these models, the
underlying structure of the video data is learned, allowing it to be used to create synthetic
video data similar to the original ones. Different types of generative video models are
available, like GANs, VAEs, CGANs and more, each of which takes a different training
approach based on its unique infrastructure.
Generative video models mostly utilize text-to-video prompts where users can enter their
requirements through text, and the model generates the video using the textual
description. Depending on your tools, generative video models also utilize sketch or image
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prompts to generate videos.
What tasks can a generative video model perform?
A wide range of activities can be carried out by generative video models, including:
1. Video synthesis: Generative video models can be used to create new video frames to
complete a sequence that has only been partially completed. This can be handy for
creating new video footage from still photographs or replacing the missing frames in
a damaged movie.
2. Video style transfer: Transferring one video style to another using generative video
models enables the creation of innovative and distinctive visual effects. For instance,
to give a video a distinct look, the style of a well-known artwork could be applied.
3. Video compression: Generative video models can be applied to video compression,
which comprises encoding the original video into a lower-dimensional
representation and decoding it to produce a synthetic video comparable to the
original. Doing this makes it possible to compress video files without compromising
on quality.
4. Video super resolution: By increasing the resolution of poor-quality videos,
generative video models can make them seem sharper and more detailed.
5. Video denoising: Noise can be removed using generative video models to make
video data clearer and simpler to watch.
6. Video prediction: To do real-time video prediction tasks like autonomous driving or
security monitoring, generative video models can be implemented to forecast the
next frames in a video. Based on the patterns and relationships discovered from the
training data, the model can interpret the currently playing video data and produce
the next frames.
Benefits of generative video models
Compared to more conventional techniques, generative video models have a number of
benefits:
1. Efficiency: Generative video models can be taught on massive datasets of videos and
images to produce new videos quickly and efficiently in real time. This makes it
possible to swiftly and affordably produce large volumes of fresh video material.
2. Customization: With the right adjustments, generative video models can produce
video material that is adapted to a variety of needs, including style, genre, and tone.
This enables the development of video content with more freedom and flexibility.
3. Diversity: Generative video models can produce a wide range of video content,
including original scenes and characters and videos created from text descriptions.
This opens up new channels for the production and dissemination of video content.
4. Data augmentation: Generative video models can produce more training data for
computer vision and machine learning models, which can help these models
perform better and become more resilient to changes in the distribution of the data.
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5. Novelty: Generative video models can produce innovative and unique video content
that is still related to the training data creating new possibilities for investigating
novel forms of storytelling and video content.
How do generative video models work?
Like any other AI model, generative video models are trained on large data sets to
produce new videos. However, the training process varies from model to model
depending on the model’s architecture. Let us understand how this may work by taking
the example of two different models: VAE and GAN.
Variational Autoencoders (VAEs)
A Variational Autoencoder (VAE) is a generative model for generating videos and images.
In a VAE, two main components are present: an encoder and a decoder. An encoder maps
a video to a lower-dimensional representation, called a latent code, while a decoder
reverses the process.
A VAE uses encoders and decoders to model the distribution of videos in training data. In
the encoder, each video is mapped into a latent code, which becomes a parameter for
parametrizing a probability distribution (such as a normal distribution). To calculate a
reconstruction loss, the decoder maps the latent code back to a video, then compares it to
the original video.
To maximize the diversity of the generated videos, the VAE encourages the latent codes to
follow the prior distribution, which minimizes the reconstruction loss. After the VAE has
been trained, it can be leveraged to generate new videos by sampling latent codes from a
prior distribution and passing them through the decoder.
Generative Adversarial Networks (GANs)
GANs are deep learning model that generates images or videos when given a text prompt.
A GAN has two core components: a generator and a discriminator. Both the generator and
the discriminator, being neural networks, process the video input to generate different
kinds of output. While the generator generates fake videos, the discriminator assesses
these videos’ originality to provide feedback to the generator.
Using a random noise vector as input, the generator in the GAN generates a video.
Discriminators take in videos as input and produce probability scores indicating the
likelihood of the video is real. Here, the generator classifies the videos as real if taken
from the training data and the video generated by the generator is stamped as fake.
Generators and discriminators have trained adversarially during training. Generators are
trained to create fake videos that discriminators cannot detect, while discriminators are
trained to identify fake videos created by generators. The generator continues this process
until it produces videos that the discriminator can no longer distinguish from actual
videos.
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Following the training process, a noise vector can be sampled and passed through the
generator to generate a brand-new video. While incorporating some randomness and
diversity, the resultant videos should reflect the characteristics of the training data.
Random data
samples
Generator
Real/training
data sample
Generated
data sample
Fine tune training
Discriminator
Fine tune training
Random data
samples
Generator classifies as
real/fake
How does a GAN model work?
LeewayHertz
How to create a generative video model?
Here, we discuss how to create a generative video model similar to the VToonify
framework that combines the advantages of StyleGAN and Toonify frameworks.
Set up the environment
The first step to creating a generative video model is setting up the environment. To set up
the environment for creating a generative video model, you must decide on the right
programming language to write codes. Here, we are moving forward with Python. Next,
you must install several software packages, including a deep learning framework such as
TensorFlow or PyTorch, and any additional libraries you will need to preprocess and
visualize your data.
Model architecture design
You cannot create a generative video model without designing the architecture of the
model. It determines the quality and capacity of the generated video sequences.
Considering the sequential nature of video data is critical when designing the architecture
of the generative model since video sequences consist of multiple frames linked by time.
Combining CNNs with RNNs or creating a custom architecture may be an option.
As we are designing a model similar to VToonify, understanding in-depth about the
framework is necessary. So, what is VToonify?
VToonify is a framework developed by MMLab@NTU for generating high-quality artistic
portrait videos. It combines the advantages of two existing frameworks: the image
translation framework and the StyleGAN-based framework. The image translation
framework supports variable input size, but achieving high-resolution and controllable
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style transfer is difficult. On the other hand, the StyleGAN-based framework is good for
high-resolution and controllable style transfer but is limited to fixed image size and may
lose details.
VToonify uses the StyleGAN model to achieve high-resolution and controllable style
transfer and removes its limitations by adapting the StyleGAN architecture into a fully
convolutional encoder-generator architecture. It uses an encoder to extract multi-scale
content features of the input frame and combines them with the StyleGAN model to
preserve the frame details and control the style. The framework has two instantiations,
namely, VToonify-T and VToonify-D, wherein the first uses Toonify and the latter follows
DualStyleGAN.
In the above code snippet, the function ‘train’ establishes various loss tensors for the
generator and the discriminator and generates a dictionary of loss values. Using the
backpropagation algorithm, the algorithm loops over the specified number of iterations
and calculates and minimizes losses.
You can find the whole set of codes to train the model here.
Model evaluation and fine-tuning
Model evaluation involves evaluating the model’s quality, efficiency, and effectiveness.
When developers evaluate a model carefully, they can identify areas for improvement and
fine-tune its parameters to improve its functionality. This process involves accessing the
quality of the generated video sequences using quantitative metrics such as structural
similarity index (SSIM), Mean Squared Error (MSE) or peak signal-to-noise ratio (PSNR)
and visually inspecting the generated video sequences.
Based on the evaluation results, fine-tune the model by adjusting the architecture,
configuration, or training process to improve its performance. It would be best to
optimize the hyperparameters, which involves adjusting the loss function, fine-tuning the
optimization algorithm and tweaking the model’s parameters to enhance the generative
video model’s performance.
Develop web UI
Building a web User Interface (UI) is necessary if your project needs the end-users to
interact with the video model. It enables users to feed input parameters like effects, style
types, image rescale, style degree or more. For this, you must design the layout,
topography, colors and other visual elements based on your set parameters.
Now, develop the front end as per the design. Once the UI is developed, test it thoroughly
to make it free of bugs and optimize the functionality. You can also use Gradio UI to build
custom UI for the project without coding requirements.
Deployment
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Once the model is trained and fine-tuned and the web UI is built, the model needs to be
deployed to a production environment for generating new videos. Integration with a
mobile or web app, setting up a data processing and streaming pipeline, and configuring
the hardware and software infrastructure may be required to deploy the model based on
the requirement.
Wrapping up
The steps involved in creating a generative video model are complex and consist of
preprocessing the video dataset and designing the model architecture to adding layers to
the basic architecture and training and evaluating the model. Generative Adversarial
Networks (GANs) or Variational Autoencoders (VAEs) are frequently used as the
foundation architecture, and the model’s capacity and complexity can be increased by
including Convolutional, Pooling, Recurrent, or Dense layers.
There are several applications for generative video models, such as video synthesis, video
toonification, and video style transfer. Existing image-oriented models can be trained to
produce high-quality, artistic videos with adaptable style settings. The field of generative
video models is rapidly evolving, and new techniques and models are continually being
developed to improve the quality and flexibility of the generated videos.
Fascinated by a generative video model’s capabilities and want to leverage its power to
level up your business? Contact LeewayHertz today to start building your own
generative video model and transform your vision into reality!