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SotA/Natural Language Processing/Visual Question Answering (VQA)/VLM2-Bench

Visual Question Answering (VQA) on VLM2-Bench

Metric: GC-mat (higher is better)

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#Model↕GC-mat▼Extra DataPaperDate↕Code
1GPT-4o37.45NoGPT-4o System Card2024-10-25-
2Qwen2.5-VL-7B35.91NoQwen2.5-VL Technical Report2025-02-19Code
3InternVL2.5-26B30.5NoExpanding Performance Boundaries of Open-Source ...2024-12-06Code
4Qwen2-VL-7B27.8NoQwen2-VL: Enhancing Vision-Language Model's Perc...2024-09-18Code
5InternVL2.5-8B21.24NoExpanding Performance Boundaries of Open-Source ...2024-12-06Code
6LLaVA-Video-7B18.53NoVideo Instruction Tuning With Synthetic Data2024-10-03-
7mPLUG-Owl3-7B17.37NomPLUG-Owl3: Towards Long Image-Sequence Understa...2024-08-09Code
8LLaVA-OneVision-7B16.6NoLLaVA-OneVision: Easy Visual Task Transfer2024-08-06Code
9LongVA-7B14.29NoLong Context Transfer from Language to Vision2024-06-24Code