Importance of Image-Text Datasets
Web-crawled image-text datasets are essential for training vision-language models. They help improve tasks like image captioning and visual question answering. However, these datasets often contain noise and low-quality associations between images and text, which limits model performance, especially in cross-modal retrieval tasks. The large computational cost involved in handling these datasets makes it crucial to find better training methods.
Introducing Synthetic Captions
To tackle these issues, researchers have started using synthetic captions generated by multimodal large language models (MLLMs) instead of noisy web-crawled captions. Synthetic captions have shown to enhance model performance, as seen in frameworks like VeCLIP and Recap-DataComp-1B. Yet, existing methods still face challenges, including high computational costs, difficulties in scaling for complex architectures, and inefficiencies in utilizing all information from synthetic captions.
CLIPS: A New Framework
Researchers from UC Santa Cruz and the University of Edinburgh have developed CLIPS, an advanced vision-language training framework that optimizes the use of synthetic captions. Here are the key solutions CLIPS offers:
1. Partial Synthetic Captions for Contrastive Learning
CLIPS employs a technique that focuses on partial synthetic captions for contrastive learning. By sampling parts of the captions, it reduces input token length while maintaining or enhancing performance. This approach leads to improved retrieval accuracy and lower computational costs.
2. Autoregressive Caption Generation
CLIPS also uses an autoregressive generator that creates whole synthetic captions based on web-crawled captions and images. This method enriches the connection between image and text, ensuring effective use of synthetic data.
Technical Implementation
The framework preprocesses synthetic captions with a sub-caption masking strategy, retaining about 32 tokens, equivalent to one or two sentences. It uses a multi-positive contrastive loss to align original and shortened captions for better efficiency. The generative framework employs an autoregressive decoder to produce complete synthetic captions, guided by a customized token interaction mask.
Outstanding Performance
CLIPS has demonstrated state-of-the-art performance in various tasks. In MSCOCO, it achieved over 5% improvement in text-to-image retrieval and 3% in image-to-text retrieval compared to earlier methods. Similarly, on Flickr30K, it showed superior retrieval accuracy in both directions. Smaller models trained with CLIPS even outperformed larger models from other frameworks, highlighting its scalability and effectiveness. Additionally, integrating CLIPS with multimodal large language models enhances their performance across several benchmarks.
Conclusion
CLIPS represents a significant advancement in vision-language training, addressing challenges faced by prior models. By utilizing synthetic captions and innovative learning techniques, it sets new benchmarks in cross-modal retrieval tasks, ensuring scalability, computational efficiency, and improved multimodal understanding.
Explore Further
Check out the Paper, Code, and Model on Hugging Face. Special thanks to the researchers behind this project. Don’t forget to follow us on Twitter, join our Telegram Channel, and connect with our LinkedIn Group. If you appreciate our work, subscribe to our newsletter and join our 60k+ ML SubReddit.
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