Image Paragraph Generator Using Visual Clues
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Solution Overview
Problem
Current vision-based caption generation models require large volumes of expensive and labor-intensive training data to produce simple captions, which are not descriptive or coherent enough for certain applications.
Innovation Solution
A system that uses a first vision language model to generate visual information for an image, a generative language model to produce multiple image story caption candidates, and a second vision language model to select a detailed and accurate caption, leveraging visual clues to reduce the need for extensive training data and improve computing efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If an end-to-end vision-based caption generation model is trained on large volumes of image-story pairs, then the model can generate text descriptions, but the process becomes expensive and labor-intensive due to the large amount of training data required
Solution Approach 1:
The system segments the caption generation process into three distinct components: (1) a vision language model that generates visual information and visual clues from the image, (2) a generative language model that creates multiple candidate captions from the visual clues, and (3) a second vision language model that evaluates and selects the best caption. This segmentation allows each component to be optimized independently and reduces the overall training data requirements compared to a monolithic end-to-end model.
Solution Approach 2:
The patent introduces visual clues as an intermediary representation that bridges the vision language model and the generative language model. The visual clues serve as a compact, structured summary of the image content that guides the generative model without requiring it to learn from large volumes of raw image-story pairs directly, thereby reducing training data requirements while maintaining generation quality.
2Productivity
If an end-to-end model is used for vision-based caption generation, then text description can be produced, but computing resources required are excessively large
Solution Approach 1:
By dividing the caption generation task into separate vision-language translation and language generation stages, the system avoids the computational burden of training a single large end-to-end model. Each component can be trained and processed more efficiently, reducing overall computing resource consumption while maintaining generation capability.
Solution Approach 2:
The vision language model performs preliminary extraction of visual information and generation of visual clues before the generative language model creates captions. This preliminary action pre-processes the image data into a compact representation, reducing the computational load on subsequent generation and evaluation stages.
3Productivity
If simple captioning is used, then processing is faster and requires fewer resources, but the output lacks coherence and descriptive quality for certain scenarios
Solution Approach 1:
The system generates multiple candidate captions (excessive action) rather than a single caption, then evaluates and selects the best one. This approach ensures higher quality and coherence by considering multiple possibilities, while the evaluation step filters out inferior candidates to maintain processing efficiency.
Solution Approach 2:
The second vision language model provides feedback by evaluating the candidate captions against the original image and visual clues, selecting the caption that best aligns with the visual content. This feedback mechanism ensures coherence and descriptive quality without requiring excessively complex processing.
Data Source
AI summary
Example solutions for image paragraph captioning use a first vision language model to generate visual information (comprising text) for an image. The visual information may include tags, an initial image caption, and information on objects within the image (e.g., further tags and captions, and object attributes and locations within the image). In some examples, the visual information further includes visual clues. A generative language model generates a plurality of image story caption candidates (e.g., descriptive paragraphs) from the visual information. A second vision language model evaluates the plurality of image story caption candidates and selects a caption as the final output caption.