Neural Network Image Captioning With Feature-Matched Templates
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current image captioning systems face challenges in compositionality and naturalness, dataset bias, and inadequate evaluation metrics, leading to resource-intensive and often incorrect or biased captions.
Innovation Solution
An image system utilizing a neural network model and templates to generate captions by detecting features with a classifier model, replacing keywords with tags, grouping sentences into clusters, and selecting representative templates to populate with detected features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional sequential captioning systems are used, then captions can be generated word by word, but the system consumes excessive computing resources and produces syntactically correct but semantically irrelevant captions
Solution Approach 1:
The patent segments the captioning process into distinct phases: template selection based on image features, and then caption generation using the selected template. This segmentation allows the system to avoid resource-intensive sequential generation while maintaining semantic relevance by matching templates to detected image features before populating them with specific details.
Solution Approach 2:
The system performs preliminary actions by detecting image features and selecting appropriate templates before generating the actual caption. This preliminary template selection based on feature detection ensures that the caption structure is predetermined and semantically appropriate, reducing the computational burden during the actual generation phase.
2Reliability
If traditional image captioning systems are used, then captions can be generated, but they suffer from dataset bias and produce incorrect or biased captions requiring resource-intensive corrections
Solution Approach 1:
The patent incorporates feedback mechanisms where the system detects image features, selects templates that match these features, and generates captions accordingly. This feedback loop ensures that captions are aligned with actual image content, reducing bias and errors. The template selection process acts as a feedback mechanism to guide caption generation toward accuracy.
Solution Approach 2:
The system changes parameters by using template-based generation with feature detection rather than pure sequential generation. This parameter change in the generation approach reduces bias by anchoring captions to detected image features, improving correctness without requiring extensive corrections.
3Ease of operation
If sequential word-by-word generation is used, then captions can be generated, but they lack compositionality and naturalness
Solution Approach 1:
The patent segments the captioning task into template selection and template population phases. This segmentation enables the system to use pre-defined natural language templates that inherently possess compositionality and naturalness, avoiding the need for complex sequential generation while maintaining high caption quality.
Solution Approach 2:
The system uses template copying where pre-defined natural language templates are selected and populated with image-specific features. This copying approach preserves the naturalness and compositionality of human-written templates while adapting them to specific images, avoiding the need for complex generation algorithms.
Data Source
AI summary
A device may receive a plurality of narratives associated with a plurality of scenes and an image identifying a scene not included in the plurality of scenes, and may process the image, with a classifier model, to detect a plurality of features in the image. The device may replace keywords in the plurality of narratives, with tags, to generate a plurality of sentences, and may group similar sentences of the plurality of sentences, based on a defined measure of dissimilarity, into clusters of templates. The device may select a candidate template from each of the clusters to generate a set of candidate templates, and may select a template from the set of candidate templates. The device may populate tags of the template with the plurality of features detected in the image to generate an image caption, and may provide the image and the image caption for display.