POSTECH RESEARCH AND BUSINESS DEVELOPMENT FOUNDATION (20240346722). IMAGE GENERATING APPARATUS, DEEP LEARNING TRAINING METHOD, AND STORAGE MEDIUM STORING INSTRUCTIONS TO PERFORM THUMBNAIL GENERATING METHOD simplified abstract
Contents
- 1 IMAGE GENERATING APPARATUS, DEEP LEARNING TRAINING METHOD, AND STORAGE MEDIUM STORING INSTRUCTIONS TO PERFORM THUMBNAIL GENERATING METHOD
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 IMAGE GENERATING APPARATUS, DEEP LEARNING TRAINING METHOD, AND STORAGE MEDIUM STORING INSTRUCTIONS TO PERFORM THUMBNAIL GENERATING METHOD - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Key Features and Innovation
- 1.6 Potential Applications
- 1.7 Problems Solved
- 1.8 Benefits
- 1.9 Commercial Applications
- 1.10 Prior Art
- 1.11 Frequently Updated Research
- 1.12 Questions about Audio-Driven Image Generation
- 1.13 Original Abstract Submitted
IMAGE GENERATING APPARATUS, DEEP LEARNING TRAINING METHOD, AND STORAGE MEDIUM STORING INSTRUCTIONS TO PERFORM THUMBNAIL GENERATING METHOD
Organization Name
POSTECH RESEARCH AND BUSINESS DEVELOPMENT FOUNDATION
Inventor(s)
IMAGE GENERATING APPARATUS, DEEP LEARNING TRAINING METHOD, AND STORAGE MEDIUM STORING INSTRUCTIONS TO PERFORM THUMBNAIL GENERATING METHOD - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240346722 titled 'IMAGE GENERATING APPARATUS, DEEP LEARNING TRAINING METHOD, AND STORAGE MEDIUM STORING INSTRUCTIONS TO PERFORM THUMBNAIL GENERATING METHOD
Simplified Explanation
The patent application describes a method for training a model that generates an image from an audio input by aligning audio and image feature vectors in an embedding space.
- Select frames from a video based on the correlation between audio and image.
- Extract image and audio information from the selected frames.
- Train a model to extract audio feature vectors aligned with pre-trained image feature vectors.
Key Features and Innovation
- Training a model to generate images from audio inputs.
- Aligning audio and image feature vectors in an embedding space.
- Extracting audio and image information from video frames.
Potential Applications
This technology could be used in:
- Creating visual representations of audio content.
- Enhancing multimedia content creation.
- Improving accessibility for visually impaired individuals.
Problems Solved
- Generating images from audio inputs.
- Aligning audio and image features for accurate representation.
- Enhancing the understanding and accessibility of audio content.
Benefits
- Improved multimedia content creation.
- Enhanced accessibility for visually impaired individuals.
- Efficient generation of visual content from audio inputs.
Commercial Applications
Title: Audio-Driven Image Generation Technology This technology could be utilized in:
- Media production for creating visual content from audio.
- Accessibility tools for converting audio information into visual representations.
- Entertainment industry for enhancing audio-visual experiences.
Prior Art
Further research can be conducted in the field of audio-driven image generation to explore existing technologies and methodologies.
Frequently Updated Research
Stay updated on advancements in audio-driven image generation technology to leverage the latest developments in the field.
Questions about Audio-Driven Image Generation
1. How does this technology improve multimedia content creation?
- This technology enhances multimedia content creation by allowing the generation of visual representations from audio inputs.
2. What are the potential applications of aligning audio and image feature vectors?
- Aligning audio and image feature vectors enables accurate representation of audio content through visual images.
Original Abstract Submitted
there is provided a method for training an image generating model that generates an image from an audio. the method includes selecting at least one frame from a video including a plurality of frames based on a correlation between an audio and an image of each frame; extracting image information and audio information from each of the selected at least one frame; and training an audio feature vector extracting model that extracts an audio feature vector from the audio information, wherein the audio feature vector is aligned within an embedding space with an image feature vector extracted from the image information by a pre-trained image feature vector extracting model.
