Image Caption Generation With User-Controlled Detail Levels
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Solution Overview
Problem
Existing image captioning technologies generate descriptions with a fixed level of detail, failing to accommodate user preferences for varying levels of detail in image descriptions.
Innovation Solution
A machine learning model is trained to generate text descriptions for images with adjustable detail levels based on user input, using a pipeline architecture that includes region convolutional neural networks (R-CNN) for object identification and a language model like BERT or LSTM to produce descriptions with varying specificity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a fixed set of categories is used for image classification, then the classification process is simple and fast, but the description cannot express richer concepts or vary in detail level
Solution Approach 1:
The patent applies dynamics by making the description generation process adjustable and flexible. The system allows users to specify different detail levels (e.g., short, medium, long descriptions) and dynamically generates appropriate descriptions. The model can adapt its output based on user input, transitioning between different levels of description detail rather than being fixed to a single category set.
Solution Approach 2:
The patent changes the parameter of description length and detail level. By introducing a detail level parameter that can be adjusted by users, the system generates descriptions with varying amounts of information. This parameter change allows the same model to produce both concise and detailed descriptions depending on user requirements.
2Loss of information
If detailed descriptions are generated for all image regions, then the description completeness is improved, but the processing time and computational resources increase
Solution Approach 1:
The patent applies partial action by selectively generating descriptions for different regions of the image based on the specified detail level. For lower detail levels, the model focuses on describing only the most prominent or important regions. For higher detail levels, it progressively includes more regions and finer details. This selective approach ensures that processing time is optimized while still providing sufficient description completeness for the given detail level.
3Ease of operation
If a single description length is used for all images, then the system is simpler to implement, but it cannot meet diverse user requirements for description detail
Solution Approach 1:
The patent applies universality by designing a multi-functional description generation system. The same underlying model can generate multiple types of descriptions (short summaries, medium-length descriptions, and detailed captions) based on a single unified architecture. This allows the system to serve diverse user needs without requiring separate models for each description type, balancing ease of operation with controlled complexity.
Data Source
AI summary
One or more aspects of the method, apparatus, and non-transitory computer readable medium include obtaining an image and a detail level, wherein the detail level comprises a value indicating a level of detail for a description of the image. One or more aspects of the method, apparatus, and non-transitory computer readable medium further include identifying a set of regions for the image based on the detail level using a machine learning model, and generating a description for the image based on the set of regions, wherein an amount of detail in the description is based on the detail level.