18546842. METHOD AND APPARATUS FOR VISUAL REASONING simplified abstract (Robert Bosch GmbH)
Contents
METHOD AND APPARATUS FOR VISUAL REASONING
Organization Name
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METHOD AND APPARATUS FOR VISUAL REASONING - A simplified explanation of the abstract
This abstract first appeared for US patent application 18546842 titled 'METHOD AND APPARATUS FOR VISUAL REASONING
The patent application describes a method for visual reasoning using a Probabilistic Generative Model (PGM) and a set of modules.
- The method involves providing a network with sets of inputs and outputs based on visual information.
- It determines a posterior distribution over combinations of modules through the PGM.
- Domain knowledge is applied as posterior regularization constraints on the determined distribution.
Potential Applications:
- Visual recognition systems
- Image processing algorithms
- Pattern recognition technologies
Problems Solved:
- Enhancing visual reasoning capabilities
- Improving accuracy in image analysis
- Optimizing decision-making processes based on visual data
Benefits:
- Increased efficiency in visual data processing
- Enhanced accuracy in pattern recognition
- Improved performance of visual recognition systems
Commercial Applications:
- Visual search engines
- Autonomous vehicles
- Medical imaging technologies
Questions about Visual Reasoning: 1. How does the method improve decision-making processes based on visual information? 2. What are the key advantages of using a Probabilistic Generative Model in visual reasoning?
Frequently Updated Research: Stay updated on advancements in visual reasoning technologies and applications to leverage the latest innovations in the field.
Original Abstract Submitted
A method for visual reasoning. The method includes: providing a network with sets of inputs and sets of outputs, wherein each set of inputs of the sets of inputs mapping to one of a set of outputs corresponding to the set of inputs based on visual information on the set of inputs, and wherein the network comprising a Probabilistic Generative Model (PGM) and a set of modules; determining a posterior distribution over combinations of one or more modules of the set of modules through the PGM, based on the provided sets of inputs and sets of outputs; and applying domain knowledge as one or more posterior regularization constraints on the determined posterior distribution.
