Perturbation-Aware Caption Metric for Reliable Image Scoring
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Solution Overview
Problem
Existing image caption evaluation systems fail to accurately distinguish between original and perturbed captions, leading to unreliable evaluation scores due to their inability to capture lexical noise, and often require user-provided reference captions or labeled data.
Innovation Solution
A text encoder is trained to recognize perturbations in image captions by generating embeddings for ground truth and perturbed captions, using cosine similarity losses to differentiate between them, without requiring labeled data, and is further trained to handle multiple languages through a pre-training phase.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing image caption evaluation systems use traditional metrics, then the evaluation process is simple, but the evaluation scores are unreliable because they cannot distinguish between original and perturbed captions
Solution Approach 1:
The patent introduces a text encoder as an intermediary component that transforms captions into embeddings, which are then compared using cosine similarity. This intermediary mechanism enables reliable distinction between original and perturbed captions by capturing semantic relationships that traditional metrics miss, while keeping the overall system architecture manageable through modular design.
Solution Approach 2:
The patent transitions from traditional scalar-based evaluation metrics to a vector embedding space approach. By representing captions as high-dimensional vectors and using cosine similarity, the system operates in a new dimensional space that captures lexical noise and semantic meaning more effectively, improving evaluation precision without proportionally increasing complexity.
2Ease of operation
If reference-based metrics are used to evaluate image captions, then evaluation can be performed, but user-provided reference captions are required which increases operational complexity
Solution Approach 1:
The patent implements a self-service evaluation mechanism where the system generates its own reference embeddings from ground truth captions during training. The text encoder learns to distinguish perturbed from original captions autonomously using cosine similarity losses, eliminating the need for external user-provided references and making the system operationally simpler while maintaining accuracy.
3Measurement precision
If the evaluation system is trained to recognize perturbations, then distinction between original and perturbed captions improves, but training data requirements increase
Solution Approach 1:
The patent applies preliminary action by pre-training the text encoder on general language tasks and multilingual data before fine-tuning for caption evaluation. This pre-training phase establishes robust embedding representations that generalize well, reducing the amount of specific training data needed to achieve high accuracy in distinguishing perturbed from original captions.
Solution Approach 2:
The patent utilizes parameter changes by adjusting the cosine similarity threshold and embedding dimensionality to optimize the balance between training data requirements and distinction accuracy. By tuning these parameters, the system achieves high measurement precision with efficient use of training data, avoiding the need for excessively large datasets.
Data Source
AI summary
Embodiments are disclosed for training an image caption evaluation system to perform evaluations of image captions. In particular, in one or more embodiments, the disclosed systems and methods comprise receiving a training image, a ground truth image caption for the training image, and a perturbed image caption for the training image, where the perturbed image caption includes modifications to the ground truth image caption. The disclosed systems and methods further comprise generating, by a visual encoder, a visual embedding representation of the training image and generating, by a perturbation-aware text encoder, a first text embedding for the ground truth image caption and a second text embedding for the perturbed image caption. The disclosed systems and methods further comprise computing losses between the visual embedding, the first text embedding, and the second text embedding and training the perturbation-aware text encoder based on the computed losses.