Image-to-Text Encoding for Low-Resource Caption Generation
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Solution Overview
Problem
Existing image captioning technologies require significant memory and calculation resources and are limited to specific domains, making it difficult to generate natural language descriptions for images without labels.
Innovation Solution
An apparatus and method that utilize a processor to generate encoding information for images, extract text information based on the degree of association with encoding information, and combine image and text to create synthetic data, reducing the need for labeled data and training time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional OCR and image captioning technologies are used, then text extraction and image description capabilities are provided, but significant memory and calculation resources are required and training time is excessive
Solution Approach 1:
The patent segments the image processing task into distinct modules: a codebook generation module that creates encoding information from images, and a text extraction module that translates this encoding into text descriptions. This segmentation allows each module to be optimized independently, reducing overall computational resource requirements while maintaining effective text extraction capability.
Solution Approach 2:
The patent performs preliminary action by pre-generating encoding information for images using a codebook before actual text extraction is needed. This pre-processing step transforms images into a compressed representation format that can be quickly translated into text, significantly reducing the computational resources required during the main text extraction operation.
2Adaptability or versatility
If traditional image captioning models are trained, then natural language description generation is achieved, but training is possible for limited domains only and requires huge amounts of memory
Solution Approach 1:
The patent creates a universal codebook that can encode images from multiple domains simultaneously. The codebook generates encoding information that can be translated into text descriptions for various types of images (natural scenes, objects, scenes with text, etc.), making the system adaptable to different domains without requiring separate training for each domain.
Solution Approach 2:
The patent uses a codebook that copies and compresses the essential features of images into a standardized representation format. This copied encoding information can then be translated into text descriptions, allowing the system to handle diverse image types without needing to retrain for each specific domain, thereby expanding adaptability without proportionally increasing training data requirements.
3Measurement precision
If existing image processing technologies are applied, then object recognition is achieved, but the ability to describe the image itself rather than just detectable objects is limited
Solution Approach 1:
The patent merges object detection capabilities with overall image description capabilities into a unified system. The codebook captures both the individual objects present in an image and the overall image characteristics, allowing the text extraction module to generate descriptions that include both detected objects and general image information, thereby enhancing ease of operation for comprehensive image understanding.
Data Source
AI summary
An apparatus for generating text from an image may comprise: a memory configured to store at least one instruction; and a processor configured to execute the at least one instruction, wherein the processor is further configured to generate encoding information for an image based on the image and extract text information related to content of the image based on a degree of association with the encoding information.