FG-CLIP 2: A Bilingual Fine-grained Vision-Language Alignment Model
Authors: name: Chunyu Xie, name: Bin Wang, name: Fanjing Kong, name: Jincheng Li, name: Dawei Liang, name: Ji Ao, name: Dawei Leng, name: Yuhui Yin

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.