FG-CLIP 2: A Bilingual Fine-grained Vision-Language Alignment Model

• URL: http://arxiv.org/abs/2510.10921v2
• Date: Fri, 17 Oct 2025 08:47:31 GMT
• Title: FG-CLIP 2: A Bilingual Fine-grained Vision-Language Alignment Model
• Authors: Chunyu Xie, Bin Wang, Fanjing Kong, Jincheng Li, Dawei Liang, Ji Ao, Dawei Leng, Yuhui Yin,
• Abstract summary: FG-CLIP 2 is a vision-language model designed to advance fine-grained alignment for both English and Chinese.<n>Our approach leverages rich fine-grained supervision, including region-text matching and long-caption modeling.<n>We present a new benchmark for Chinese multimodal understanding, featuring long-caption retrieval and bounding box classification.
• Score: 11.423111315561151
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Fine-grained vision-language understanding requires precise alignment between visual content and linguistic descriptions, a capability that remains limited in current models, particularly in non-English settings. While models like CLIP perform well on global alignment, they often struggle to capture fine-grained details in object attributes, spatial relations, and linguistic expressions, with limited support for bilingual comprehension. To address these challenges, we introduce FG-CLIP 2, a bilingual vision-language model designed to advance fine-grained alignment for both English and Chinese. Our approach leverages rich fine-grained supervision, including region-text matching and long-caption modeling, alongside multiple discriminative objectives. We further introduce the Textual Intra-modal Contrastive (TIC) loss to better distinguish semantically similar captions. Trained on a carefully curated mixture of large-scale English and Chinese data, FG-CLIP 2 achieves powerful bilingual performance. To enable rigorous evaluation, we present a new benchmark for Chinese multimodal understanding, featuring long-caption retrieval and bounding box classification. Extensive experiments on 29 datasets across 8 tasks show that FG-CLIP 2 outperforms existing methods, achieving state-of-the-art results in both languages. We release the model, code, and benchmark to facilitate future research on bilingual fine-grained alignment.

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