Image Captioning Model Using Attention Penalty for Weakly-Supervised Training
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Solution Overview
Problem
Current image captioning models face challenges in generating accurate and relevant captions for family historical photos, requiring significant human effort and relying on expensive fine-grained annotations, with attention maps being implicitly trained and failing to align with intrinsic saliency maps from visual models.
Innovation Solution
The proposed solution involves a novel image captioning model that leverages gradient-weighted class activation mapping (Grad-CAM) and state-of-the-art bottom-up and top-down attention mechanisms to align visual attention maps with caption attention maps, incorporating an attention penalty in the loss function to improve alignment and caption quality without requiring fine-grained labels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If attention maps are implicitly trained without alignment constraints, then the model training is simpler and faster, but the caption attention maps fail to align with intrinsic saliency maps from visual models, reducing caption accuracy
Solution Approach 1:
The patent implements feedback by computing an attention penalty that compares caption attention maps with visual attention maps, and using this penalty to guide the training process. The loss function incorporates this attention penalty to provide feedback signals that adjust the attention mechanism parameters, ensuring alignment between caption and visual attention without requiring fine-grained annotations.
Solution Approach 2:
The patent changes the parameter optimization approach by adding an attention penalty term to the loss function. This modifies the gradient descent process to simultaneously optimize both caption generation accuracy and attention map alignment, achieving improved caption accuracy while maintaining relatively simple training procedures.
2Measurement precision
If fine-grained annotations are used for training, then caption accuracy improves, but the annotation cost and time consumption increase significantly
Solution Approach 1:
The patent enables the model to self-align its attention maps using only image-level annotations. The attention mechanism learns to produce caption attention maps that align with visual attention maps through the attention penalty, eliminating the need for expensive fine-grained human annotations while maintaining training effectiveness.
Solution Approach 2:
The patent introduces visual attention maps as an intermediary that bridges the gap between image-level annotations and caption generation. By using these visually-derived attention maps as a reference, the model can learn accurate captioning without requiring direct fine-grained annotations, significantly reducing annotation time and cost.
3Ease of operation
If the model focuses on all image regions equally, then the processing is simpler, but the caption relevance to specific image regions decreases
Solution Approach 1:
The patent applies local quality by making different regions of the image receive different attention weights during caption generation. The attention mechanism dynamically assigns higher weights to relevant regions and lower weights to irrelevant regions, allowing the model to focus computational resources on important areas while maintaining overall processing efficiency.
Data Source
AI summary
Techniques for training a machine-learning (ML) model for captioning images are disclosed. A plurality of feature vectors and a plurality of visual attention maps are generated by a visual model of the ML model based on an input image. Each of the plurality of feature vectors correspond to different regions of the input image. A plurality of caption attention maps are generated by an attention model of the ML model based on the plurality of feature vectors. An attention penalty is calculated based on a comparison between the caption attention maps and the visual attention maps. A loss function is calculated based on the attention penalty. One or both of the visual model and the attention model are trained using the loss function.