17971098. METHOD AND APPARATUS FOR REAL-WORLD CROSS-MODAL RETRIEVAL PROBLEMS simplified abstract (SAMSUNG ELECTRONICS CO., LTD.)
METHOD AND APPARATUS FOR REAL-WORLD CROSS-MODAL RETRIEVAL PROBLEMS
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METHOD AND APPARATUS FOR REAL-WORLD CROSS-MODAL RETRIEVAL PROBLEMS - A simplified explanation of the abstract
This abstract first appeared for US patent application 17971098 titled 'METHOD AND APPARATUS FOR REAL-WORLD CROSS-MODAL RETRIEVAL PROBLEMS
Simplified Explanation
The present patent application describes a method for training a machine learning model to solve real-world cross-modal retrieval problems, such as text-based video retrieval, sketch-based image retrieval, and image-text retrieval. The application also includes a computer-implemented method and apparatus for performing these retrieval tasks using the trained machine learning model.
- The patent application focuses on training a machine learning model to solve real-world cross-modal retrieval problems.
- The model can be used for tasks like text-based video retrieval, sketch-based image retrieval, and image-text retrieval.
- The method described in the application is computer-implemented and can be used to perform these retrieval tasks efficiently.
Potential Applications
This technology has potential applications in various fields, including:
- Multimedia search engines: The trained machine learning model can be used to build more accurate and efficient multimedia search engines that can retrieve relevant results across different modalities.
- Content-based recommendation systems: The model can be utilized to improve content-based recommendation systems by enabling cross-modal retrieval of relevant content.
- Visual and textual information retrieval: This technology can be applied in fields where visual and textual information needs to be retrieved, such as image recognition, video analysis, and natural language processing.
Problems Solved
The technology described in the patent application addresses the following problems:
- Cross-modal retrieval: It solves the challenge of retrieving relevant information across different modalities, such as text and images or videos.
- Real-world scenarios: The method is designed to handle real-world cross-modal retrieval problems, which often involve complex and diverse data.
- Efficiency and accuracy: The trained machine learning model aims to improve the efficiency and accuracy of cross-modal retrieval tasks compared to traditional methods.
Benefits
The benefits of this technology include:
- Improved search and recommendation systems: The trained model can enhance the performance of search engines and recommendation systems by providing more accurate and relevant results.
- Enhanced user experience: Users can benefit from faster and more precise retrieval of information across different modalities, leading to an improved user experience.
- Time and cost savings: The efficient cross-modal retrieval method can save time and resources in various applications, such as multimedia analysis and content recommendation.
Original Abstract Submitted
Broadly speaking, the present application generally relates to a method for training a machine learning, ML, model to perform real world cross-modal retrieval problems, and to a computer-implemented method and apparatus for performing real world cross-modal retrieval problems such as including text-based video retrieval, sketch-based image retrieval, and image-text retrieval using a trained machine learning, ML, model.
