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

Visual Question Answering (VQA) on VLM2-Bench

Metric: PC-VID (higher is better)

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#Model↕PC-VID▼Extra DataPaperDate↕Code
1GPT-4o66.75NoGPT-4o System Card2024-10-25-
2LLaVA-Video-7B59NoVideo Instruction Tuning With Synthetic Data2024-10-03-
3LLaVA-OneVision-7B47.25NoLLaVA-OneVision: Easy Visual Task Transfer2024-08-06Code
4Qwen2.5-VL-7B46.5NoQwen2.5-VL Technical Report2025-02-19Code
5InternVL2.5-26B21.75NoExpanding Performance Boundaries of Open-Source ...2024-12-06Code
6Qwen2-VL-7B16.25NoQwen2-VL: Enhancing Vision-Language Model's Perc...2024-09-18Code
7mPLUG-Owl3-7B13.5NomPLUG-Owl3: Towards Long Image-Sequence Understa...2024-08-09Code
8InternVL2.5-8B5.25NoExpanding Performance Boundaries of Open-Source ...2024-12-06Code
9LongVA-7B3.75NoLong Context Transfer from Language to Vision2024-06-24Code