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Visual Grounding On Refcoco Val
Metrics
Accuracy (%)
Results
Performance results of various models on this benchmark
| Paper Title | Repository | ||
|---|---|---|---|
| Florence-2-large-ft | 93.4 | Florence-2: Advancing a Unified Representation for a Variety of Vision Tasks | |
| mPLUG-2 | 90.33 | mPLUG-2: A Modularized Multi-modal Foundation Model Across Text, Image and Video | |
| X2-VLM (large) | 87.6 | X$^2$-VLM: All-In-One Pre-trained Model For Vision-Language Tasks | |
| XFM (base) | 86.1 | Toward Building General Foundation Models for Language, Vision, and Vision-Language Understanding Tasks | |
| X2-VLM (base) | 85.2 | X$^2$-VLM: All-In-One Pre-trained Model For Vision-Language Tasks | |
| X-VLM (base) | 84.51 | Multi-Grained Vision Language Pre-Training: Aligning Texts with Visual Concepts |
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