Adobe Inc. (20240257496). DETERMINING AUDIO AND VIDEO REPRESENTATIONS USING SELF-SUPERVISED LEARNING simplified abstract
Contents
- 1 DETERMINING AUDIO AND VIDEO REPRESENTATIONS USING SELF-SUPERVISED LEARNING
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 DETERMINING AUDIO AND VIDEO REPRESENTATIONS USING SELF-SUPERVISED LEARNING - 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 Self-Supervised Learning for Audio and Video Representation Generation
- 1.13 Original Abstract Submitted
DETERMINING AUDIO AND VIDEO REPRESENTATIONS USING SELF-SUPERVISED LEARNING
Organization Name
Inventor(s)
John Collomosse of Woking (GB)
DETERMINING AUDIO AND VIDEO REPRESENTATIONS USING SELF-SUPERVISED LEARNING - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240257496 titled 'DETERMINING AUDIO AND VIDEO REPRESENTATIONS USING SELF-SUPERVISED LEARNING
Simplified Explanation
The patent application describes a method for training a system to generate audio and video representations using self-supervised learning. This involves training machine learning models to learn short term, long term, and semantic features of input data.
- Machine learning models are trained using contrastive learning tasks and temporal learning tasks.
- The first model learns to represent the audio component of a video signal.
- The second model learns to represent the video component of the video signal.
Key Features and Innovation
- Training machine learning models using contrastive learning tasks and temporal learning tasks.
- Learning short term, long term, and semantic features of input data.
- Generating audio and video representations using self-supervised learning.
Potential Applications
This technology can be applied in various fields such as:
- Video and audio content generation.
- Speech recognition systems.
- Video surveillance and analysis.
Problems Solved
- Efficiently generating audio and video representations.
- Learning short term, long term, and semantic features of input data.
- Enhancing the capabilities of machine learning models.
Benefits
- Improved accuracy in audio and video representation generation.
- Enhanced performance of machine learning models.
- Increased efficiency in training systems.
Commercial Applications
- Title: Self-Supervised Learning for Audio and Video Representation Generation
- This technology can be utilized in industries such as entertainment, security, and communication.
- Market implications include improved content creation processes and enhanced data analysis capabilities.
Prior Art
There may be prior art related to self-supervised learning methods in audio and video representation generation. Researchers can explore academic journals, patent databases, and conferences in the field of machine learning and artificial intelligence.
Frequently Updated Research
Researchers are constantly exploring new techniques and applications of self-supervised learning in audio and video representation generation. Stay updated with recent publications and advancements in the field to leverage the latest innovations.
Questions about Self-Supervised Learning for Audio and Video Representation Generation
What are the key benefits of using self-supervised learning in audio and video representation generation?
Self-supervised learning allows for efficient training of machine learning models without the need for labeled data, leading to improved accuracy and performance in generating audio and video representations.
How does contrastive learning tasks contribute to the training of machine learning models in this context?
Contrastive learning tasks help the models learn to distinguish between different features in the input data, enhancing their ability to represent audio and video components effectively.
Original Abstract Submitted
embodiments are disclosed for training a system to generate audio and video representations using self-supervised learning. the method may include receiving a video signal including an audio component and a video component. a first machine learning model is trained to determine a representation of the audio component using a contrastive learning task and a temporal learning task. a second machine learning model to determine a representation of the video component using the contrastive learning task and the temporal learning task. by training the machine learning models using both contrastive learning tasks and temporal learning tasks, the machine learning models learn short term features, long term features, and semantic features of input data.
