Visual Grounding On Refcoco Test B
평가 지표
Accuracy (%)
평가 결과
이 벤치마크에서 각 모델의 성능 결과
모델 이름 | Accuracy (%) | Paper Title | Repository |
|---|---|---|---|
| XFM (base) | 79.8 | Toward Building General Foundation Models for Language, Vision, and Vision-Language Understanding Tasks | |
| X2-VLM (base) | 78.4 | X$^2$-VLM: All-In-One Pre-trained Model For Vision-Language Tasks | |
| mPLUG-2 | 86.05 | mPLUG-2: A Modularized Multi-modal Foundation Model Across Text, Image and Video | |
| X-VLM (base) | 76.91 | Multi-Grained Vision Language Pre-Training: Aligning Texts with Visual Concepts | |
| Florence-2-large-ft | 92.0 | Florence-2: Advancing a Unified Representation for a Variety of Vision Tasks | |
| X2-VLM (large) | 81.8 | X$^2$-VLM: All-In-One Pre-trained Model For Vision-Language Tasks |
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