Supervised Contrastive Learning for Attribute-Aware Visual Grounding
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Solution Overview
Problem
Existing referring expression comprehension models lack sufficient understanding of attributes such as color and location, and suffer from overfitting to small scale datasets, leading to poor generalization in unforeseen scenarios.
Innovation Solution
A supervised contrastive learning approach is employed to generate positive and negative image-text pairs by manipulating attributes like color and location, using data augmentation techniques to improve model understanding and generalization. This involves generating positive images and texts based on original images and texts, and constructing negative pairs by altering these attributes to enhance training.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If typical referring expression comprehension models are trained on small scale datasets, then training data requirements are reduced, but the models suffer from overfitting and lack sufficient understanding of attributes such as color and location
Solution Approach 1:
The patent creates synthetic image-text pairs by copying and manipulating existing images and texts. Specifically, it generates positive images by copying original images and creating corresponding positive texts, then constructs negative image-text pairs by manipulating attributes of these copies. This copying approach enables the model to learn from augmented data without requiring additional real-world labeled data, thereby improving generalization while working with limited training data.
Solution Approach 2:
The patent applies parameter changes by manipulating attributes such as color and location in the generated image-text pairs. The system creates negative texts by altering these parameters (e.g., changing color descriptions or location references) while keeping the base image-text structure. This parameter manipulation technique allows the model to learn robust attribute understanding and generalization by exposing it to varied attribute combinations during training, effectively addressing the overfitting problem on small datasets.
2Measurement precision
If models are trained to understand attributes like color and location, then attribute understanding improves, but the models still fail to generalize to unforeseen scenarios
Solution Approach 1:
The patent implements preliminary action by pre-generating diverse positive and negative image-text pairs before the actual training process. The system creates these synthetic training examples in advance by manipulating attributes (color, location) of original images and texts. This preliminary generation of varied training data ensures that the model encounters a wide range of attribute combinations and scenarios during training, thereby improving both attribute understanding precision and the ability to generalize to unforeseen scenarios when deployed.
Solution Approach 2:
The patent employs feedback mechanisms through contrastive learning, where the model continuously refines its understanding by comparing positive image-text pairs (correct matches) with negative image-text pairs (incorrect matches). The system uses the discrepancies between these pairs to adjust and improve the model's attribute understanding. This feedback loop enables the model to learn from its own predictions and gradually improve both measurement precision of attributes and adaptability to new scenarios, resolving the generalization problem.
3Adaptability or versatility
If data augmentation techniques are used to generate positive and negative image-text pairs, then model generalization improves, but the device complexity increases
Solution Approach 1:
The patent applies universality by designing a multi-functional training framework that handles multiple tasks within a unified approach. The same contrastive learning mechanism simultaneously generates positive image-text pairs, creates negative pairs through attribute manipulation, and trains the model on both types of pairs. This universal framework improves generalization by addressing multiple training objectives (attribute understanding, generalization, robustness) through a single integrated process, thereby reducing overall device complexity compared to separate specialized approaches.
Solution Approach 2:
The patent merges the data generation and training processes into a unified framework. Instead of separately generating augmented data and then training the model, the system combines these operations by using the contrastive learning objective to directly guide both the generation of positive/negative pairs and the training process. This merging of data augmentation and training reduces the complexity of the overall system by eliminating the need for separate data preparation pipelines and simplifying the training procedure, while still achieving improved model generalization.
Data Source
AI summary
A method of training a neural network model includes generating a positive image based on an original image, generating a positive text corresponding to the positive image based on an original text corresponding to the original image, the positive text referring to an object in the positive image, constructing a positive image-text pair for the object based on the positive image and the positive text, constructing a negative image-text pair for the object based on the original image and a negative text, the negative text not referring to the object, training the neural network model based on the positive image-text pair and the negative image-text pair to output features representing an input image-text pair, and identifying the object in the original image based on the features representing the input image-text pair.