Supervised Contrastive Learning for Multi-Modal Fine-Tuning
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Solution Overview
Problem
Current contrastive learning methods primarily focus on pre-training with multi-modal datasets for image-text pairs, but they have not been explored for fine-tuning multi-modal downstream tasks, which could further enhance performance in applications like visual question answering, graph question answering, referring expressions, and image retrieval.
Innovation Solution
The system and method for supervised contrastive learning for multi-modal tasks involve training a machine learning model using a combination of losses, including a first multi-modal representation loss from paired image-text pairs and a second loss from unpaired pairs, to achieve global cross-modality alignment and improve region-level feature alignment across images, enhancing performance in multi-modal tasks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If contrastive learning is used for pre-training with multi-modal datasets, then performance in downstream tasks is improved, but the method has not been explored for fine-tuning which could further enhance performance
Solution Approach 1:
The contrastive learning framework is extended to serve dual purposes: pre-training and fine-tuning. The same model architecture and loss function formulation are applied across both stages, allowing the system to universally handle different training phases with a unified approach, thereby improving both pre-training effectiveness and fine-tuning adaptability
2Measurement precision
If only paired image-text pairs are used for training, then the model learns alignment for matched data, but unpaired pairs are not utilized which limits the learning of global cross-modality alignment
Solution Approach 1:
The training data is segmented into two distinct components: paired image-text pairs for learning precise alignment relationships, and unpaired image-text pairs for learning global cross-modality alignment. Each segment serves a specific learning objective, and both are integrated through a combined loss function that balances their contributions to overall model performance
Solution Approach 2:
The training objective merges two previously separate learning goals into a unified framework. The total loss function combines the paired pair loss and unpaired pair loss, allowing the model to simultaneously learn both precise alignment from matched pairs and global alignment from unpaired pairs, thereby fully utilizing all available training data
Data Source
AI summary
A method includes obtaining a batch of training data including multiple paired image-text pairs and multiple unpaired image-text pairs, where each paired image-text pair and each unpaired image-text pair includes an image and a text. The method also includes training a machine learning model using the training data based on an optimization of a combination of losses. The losses include, for each paired image-text pair, (i) a first multi-modal representation loss based on the paired image-text pair and (ii) a second multi-modal representation loss based on two or more unpaired image-text pairs, selected from among the multiple unpaired image-text pairs, wherein each of the two or more unpaired image-text pairs includes either the image or the text of the paired image-text pair.