LLM-Guided Video Object Filtering for Accurate Text-Based Search
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Solution Overview
Problem
Conventional search engines struggle to accurately identify objects within video content using text-based inputs, limiting their ability to process the vast amount of digital media effectively.
Innovation Solution
A system employing large language models and detectors, such as convolutional neural networks, to detect and modify visual features in media content items based on user-defined text inputs, allowing for real-time object detection and modification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If text-based search engines are used to search video content, then the search process is simple and fast, but the ability to accurately identify objects within video content is limited
Solution Approach 1:
The patent introduces an intermediary component (object detector trained with CNN) between the text-based search interface and the video content. This intermediary translates text queries into visual object detection, enabling accurate object identification while maintaining the simplicity of text-based user interaction. The detector acts as a mediator that bridges the gap between textual search and visual content analysis.
2Productivity
If manual review of video content is performed, then accurate content identification is achieved, but the processing capacity is insufficient for large volumes of digital media
Solution Approach 1:
The patent replaces the mechanical manual review process with an automated machine learning system (object detectors trained using CNN). This substitution maintains high content identification accuracy by using sophisticated algorithms while dramatically increasing processing capacity to handle large volumes of digital media efficiently. The automated system processes video content through computational rather than human mechanisms.
3Adaptability or versatility
If conventional search engines are used, then the system is easy to operate, but the ability to search and identify objects within video content is insufficient
Solution Approach 1:
The patent implements a universal search system that handles both traditional text-based queries and visual object detection through a single integrated interface. The system maintains ease of operation by allowing users to input simple text queries while the multi-functional detector simultaneously performs text matching and visual object identification, enhancing video content search capability without increasing user interface complexity.
Data Source
AI summary
Described herein are systems and methods that detect and filter objects from media content. In particular, a detection system accesses classifiers that can detect objects when applied to a video. The detection system receives a text input from a user interface identifying filtering criteria for outputs of the classifiers and inputs the filtering criteria to a filtering language model that produces a filtering object. The detection system presents the filtering object at the user interface. In response to a user accepting the filtering object, the detection system applies the classifiers to the video, which produces a set of detected objects. The detection system applies the filtering object to the detected objects by removing a subset of the detected objects that do not satisfy the filtering criteria and presents the video with the filtered set of detected objects highlighted within the video.