Image Encoder Training via Multi-Modal Text Guidance
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Solution Overview
Problem
Current image encoding models struggle to extract feature representations that accurately represent image data, especially when limited to uni-modal data, which hinders their performance in downstream vision tasks.
Innovation Solution
The proposed solution employs a multi-modal machine learning approach using both image and text data for training image encoders through generative self-supervised learning, where image feature representations guide text generation tasks to train the image encoders, leveraging the text error as a loss function to improve feature extraction capabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If uni-modal data is used for training image encoders, then the training process is simpler, but the feature extraction capability is insufficient
Solution Approach 1:
The patent combines multi-modal data (images and text) into a unified training framework. The image encoder and text encoder are trained jointly using contrastive learning, where image features and text features are aligned in the same embedding space. This merging of modalities enables the image encoder to learn more discriminative and semantically meaningful features by leveraging the complementary information from text data, thereby resolving the contradiction between training simplicity and feature extraction capability.
Solution Approach 2:
The patent introduces a contrastive loss function as an intermediary mechanism to bridge the image and text modalities. This loss function computes the similarity between image features and text features, acting as a mediator that guides the joint training process. By using this intermediary contrastive learning objective, the system can effectively leverage multi-modal data without requiring complex architecture modifications, thus improving feature extraction capability while maintaining relative training simplicity.
2Measurement precision
If multi-modal data is used for training image encoders, then the feature extraction capability is enhanced, but the device complexity increases
Solution Approach 1:
The patent implements a universal encoder architecture that can process both image and text inputs through the same transformation pipeline. The image encoder and text encoder share similar structural components and operate in a unified embedding space, allowing the system to handle multiple modalities with a single versatile framework. This multi-functionality approach enables enhanced feature extraction from multi-modal data without proportionally increasing model complexity, as the same architectural patterns are reused across different modalities.
Solution Approach 2:
The patent employs parameter sharing and joint optimization strategies where the image encoder and text encoder are trained simultaneously with shared hyperparameters and learning rates. By changing the training parameters to support joint multi-modal training rather than separate training regimes, the system achieves enhanced feature extraction capability while controlling the increase in complexity through efficient parameter utilization and coordinated optimization.
3Adaptability or versatility
If image encoders are trained with text guidance, then the adaptability to downstream tasks is improved, but the training time increases
Solution Approach 1:
The patent performs preliminary joint training of image and text encoders using contrastive learning to pre-establish meaningful feature representations before deploying to downstream tasks. This preliminary action of pre-training with multi-modal contrastive objectives creates encoders that are already adapted to various types of visual understanding tasks, reducing the need for extensive task-specific fine-tuning later. The upfront investment in multi-modal training time pays off by significantly improving adaptability to downstream tasks.
Solution Approach 2:
The patent implements a feedback mechanism where the text encoder provides supervisory signals to guide the image encoder training through contrastive loss computation. The text features serve as feedback references that continuously guide the image feature learning process, ensuring that the image encoder learns features that are semantically aligned with text descriptions. This feedback-driven training approach improves downstream task adaptability by embedding semantic understanding directly into the feature representations during training.
Data Source
AI summary
Embodiments of the present disclosure provide a solution for image encoding learning and application. A method for image encoding learning comprises: extracting an image feature representation of a sample image using an image encoder to be trained; extracting a text feature representation of a sample text sequence using a text encoder, the sample text sequency being associated with the sample image; generating, using the text encoder, a predicted text sequence based on the text feature representation and the image feature representation; and training the image encoder at least based on a text error between the predicted text sequence and the sample text sequence.