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

arXiv: https://arxiv.org/abs/2510.10921 · HTML: https://mlsanity.com/paper/2510.10921/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 60 of its 67 references that are in the map.
- First posted on arXiv 13 Oct 2025.

## Builds on (most cited first, 30 of 60)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Visual Instruction Tuning](https://mlsanity.com/paper/2304.08485/) (2023)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Qwen2.5-VL Technical Report](https://mlsanity.com/paper/2502.13923/) (2025)
- [LAION-5B: An open large-scale dataset for training next generation image-text models](https://mlsanity.com/paper/2210.08402/) (2022)
- [Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection](https://mlsanity.com/paper/2303.05499/) (2023)
- [Sigmoid Loss for Language Image Pre-Training](https://mlsanity.com/paper/2303.15343/) (2023)
- [FlashAttention-2: Faster Attention with Better Parallelism and Work Partitioning](https://mlsanity.com/paper/2307.08691/) (2023)
- [MMBench: Is Your Multi-modal Model an All-around Player?](https://mlsanity.com/paper/2307.06281/) (2023)
- [MMMU: A Massive Multi-discipline Multimodal Understanding and Reasoning Benchmark for Expert AGI](https://mlsanity.com/paper/2311.16502/) (2023)
- [InternVL3: Exploring Advanced Training and Test-Time Recipes for Open-Source Multimodal Models](https://mlsanity.com/paper/2504.10479/) (2025)
- [Gemma 3 Technical Report](https://mlsanity.com/paper/2503.19786/) (2025)
- [Towards VQA Models That Can Read](https://mlsanity.com/paper/1904.08920/) (2019)
- [Do ImageNet Classifiers Generalize to ImageNet?](https://mlsanity.com/paper/1902.10811/) (2019)
- [ZeRO: Memory Optimizations Toward Training Trillion Parameter Models](https://mlsanity.com/paper/1910.02054/) (2019)
- [LVIS: A Dataset for Large Vocabulary Instance Segmentation](https://mlsanity.com/paper/1908.03195/) (2019)
- [SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features](https://mlsanity.com/paper/2502.14786/) (2025)
- [Grounded Language-Image Pre-training](https://mlsanity.com/paper/2112.03857/) (2021)
- [mPLUG-Owl: Modularization Empowers Large Language Models with Multimodality](https://mlsanity.com/paper/2304.14178/) (2023)
- [ShareGPT4V: Improving Large Multi-Modal Models with Better Captions](https://mlsanity.com/paper/2311.12793/) (2023)
- [Scaling Vision Transformers](https://mlsanity.com/paper/2106.04560/) (2021)
- [Gemma: Open Models Based on Gemini Research and Technology](https://mlsanity.com/paper/2403.08295/) (2024)
- [EVA-CLIP: Improved Training Techniques for CLIP at Scale](https://mlsanity.com/paper/2303.15389/) (2023)
- [YOLO-World: Real-Time Open-Vocabulary Object Detection](https://mlsanity.com/paper/2401.17270/) (2024)
- [V*: Guided Visual Search as a Core Mechanism in Multimodal LLMs](https://mlsanity.com/paper/2312.14135/) (2023)
- [GLM-4.5V and GLM-4.1V-Thinking: Towards Versatile Multimodal Reasoning with Scalable Reinforcement Learning](https://mlsanity.com/paper/2507.01006/) (2025)
- [Long-CLIP: Unlocking the Long-Text Capability of CLIP](https://mlsanity.com/paper/2403.15378/) (2024)
- [Demystifying CLIP Data](https://mlsanity.com/paper/2309.16671/) (2023)
- [Data Filtering Networks](https://mlsanity.com/paper/2309.17425/) (2023)

## Built on by (most cited first, 13 of 13)

- [WeDetect: Fast Open-Vocabulary Object Detection as Retrieval](https://mlsanity.com/paper/2512.12309/) (2025)
- [LoCoT2V-Bench: Benchmarking Long-Form and Complex Text-to-Video Generation](https://mlsanity.com/paper/2510.26412/) (2025)
- [i1: A Simple and Fully Open Recipe for Strong Text-to-Image Models](https://mlsanity.com/paper/2606.11289/) (2026)
- [PixVerve: Advancing Native UHR Image Generation to 100MP with a Large-Scale High-Quality Dataset](https://mlsanity.com/paper/2605.20147/) (2026)
- [WeGenBench: A Multidimensional Diagnostic Benchmark towards Text-to-Image Model Optimization](https://mlsanity.com/paper/2606.20100/) (2026)
- [From Panel to Pixel: Zoom-In Vision-Language Pretraining from Biomedical Scientific Literature](https://mlsanity.com/paper/2512.02566/) (2025)
- [DanQing: An Up-to-Date Large-Scale Chinese Vision-Language Pre-training Dataset](https://mlsanity.com/paper/2601.10305/) (2026)
- [ObjEmbed: Towards Universal Multimodal Object Embeddings](https://mlsanity.com/paper/2602.01753/) (2026)
- [Evaluating Remote Sensing Image Captions Beyond Metric Biases](https://mlsanity.com/paper/2604.22855/) (2026)
- [PixVL: Self-Supervised Training of Pixel-Level MLLMs via a Unified Mask--Text Consistency Cycle](https://mlsanity.com/paper/2608.01354/) (2026)
- [TDDN: Text-aligned Diffused DINO Network for Puzzle Understanding](https://mlsanity.com/paper/2609.07937/) (2026)
- [Grounded Product Understanding in Livestream Videos](https://mlsanity.com/paper/2609.20508/) (2026)
- [Semantic Capability Acquisition and Specialization During Vision-Language Model Fine-Tuning](https://mlsanity.com/paper/2610.07385/) (2026)

## Abstract (arXiv)

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, including a newly released 12M Chinese region-text dataset, 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 vision-language alignment.