FAST-GOAL: Fast and Efficient Global-local Object Alignment Learning
The paper presents FAST-GOAL, a method designed to improve the performance of vision-language models like CLIP when handling lengthy text descriptions. It introduces two key components: Fast Local Image-Sentence Matching and Token Similarity-based Learning, which enhance semantic alignment between images and text. The authors also introduce a new dataset, GLIT100k, to support their findings and demonstrate significant improvements in model adaptation to detailed textual descriptions.
- ▪FAST-GOAL is an efficient fine-tuning method for vision-language models.
- ▪The method includes Fast Local Image-Sentence Matching and Token Similarity-based Learning.
- ▪GLIT100k is a new dataset introduced to provide global image-lengthy caption pairs.
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Computer Science > Artificial Intelligence arXiv:2605.26615 (cs) [Submitted on 26 May 2026] Title:FAST-GOAL: Fast and Efficient Global-local Object Alignment Learning Authors:Hyungyu Choi, Young Kyun Jang, Chanho Eom View a PDF of the paper titled FAST-GOAL: Fast and Efficient Global-local Object Alignment Learning, by Hyungyu Choi and 2 other authors View PDF HTML (experimental) Abstract:Vision-language models such as CLIP have shown impressive capabilities in aligning images and text, but they often struggle with lengthy and detailed text descriptions due to pre-training on short and concise captions.
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