FG-CLIP: Fine-Grained Visual and Textual Alignment
Chunyu Xie, Bin Wang, Fanjing Kong, Jincheng Li, Dawei Liang, Gengshen Zhang, Dawei Leng, Yuhui Yin
2025-05-09
Summary
This paper talks about FG-CLIP, a new method that helps AI models better match and understand small details between pictures and text, making them more accurate in tasks that involve both images and words.
What's the problem?
The problem is that most AI models struggle to notice and connect fine details between what they see in an image and what is described in text. This makes it hard for them to perform well on tasks that require close attention to specific features or differences.
What's the solution?
The researchers improved the training process by using a large, high-quality dataset with very detailed captions and by including tricky examples where images and texts are similar but not exact matches. This helps the AI learn to tell the difference and align images with the right descriptions more precisely.
Why it matters?
This matters because it makes AI much better at understanding and connecting images and text, which is useful for things like search engines, digital assistants, and any technology that needs to accurately link pictures with descriptions or instructions.
Abstract
FG-CLIP enhances fine-grained understanding in multimodal tasks by leveraging large multimodal models, a high-quality dataset with detailed captions, and hard fine-grained negative samples.