Core Concepts
Long captions in language-image pre-training enhance model performance across various downstream tasks.
Abstract
The content introduces DreamLIP, a method that utilizes long captions in language-image pre-training to improve model performance. It discusses the importance of detailed descriptions for images and how long captions can benefit vision-language models. The study includes experiments on image-text retrieval, semantic segmentation, and image understanding in MLLM, showcasing the superiority of DreamLIP over existing alternatives.
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Introduction
- Language-image pre-training relies on precise text descriptions.
- Rich image content requires lengthy captions for accurate description.
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Data Extraction
- "Our model trained with 30M image-text pairs achieves on par or even better performance than CLIP trained with 400M pairs."
- "Experimental results demonstrate the consistent superiority of our method, highlighting its fine-grained representational capacity."
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Experiments
- Image-Text Retrieval: DreamLIP outperforms CLIP on tasks like COCO and Flickr30K.
- Semantic Segmentation: DreamLIP surpasses CLIP across different datasets.
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Ablation Studies
- Effectiveness of Components: Short captions, long captions, and subcaption-specific grouping loss contribute to improved performance.
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Visualization
- Qualitative visualization shows the impact of DreamLIP on semantic segmentation and image-text retrieval.
Stats
"Our model trained with 30M image-text pairs achieves on par or even better performance than CLIP trained with 400M pairs."
Quotes
"Thanks to long captions, our model can achieve better performance than CLIP trained by 400M image-text datasets."