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Visual Question Answering

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TitleStatusHype
Domain-robust VQA with diverse datasets and methods but no target labels—0
How to Design Sample and Computationally Efficient VQA Models—0
Domain Adaptation of VLM for Soccer Video Understanding—0
Do Explanations make VQA Models more Predictable to a Human?—0
Adversarial Regularization for Visual Question Answering: Strengths, Shortcomings, and Side Effects—0
How to find a good image-text embedding for remote sensing visual question answering?—0
How Transferable are Reasoning Patterns in VQA?—0
Does Object Grounding Really Reduce Hallucination of Large Vision-Language Models?—0
Does my multimodal model learn cross-modal interactions? It's harder to tell than you might think!—0
Boosting Cross-task Transferability of Adversarial Patches with Visual Relations—0
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All datasetsMM-VetViP-BenchVQA v2 test-devBenchLMMMMBenchV*benchVQA v2 valMSRVTT-QAVQA v2 test-stdMMHal-BenchMSVD-QAPlotQA-D1

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1MMCTAgent (GPT-4 + GPT-4V)GPT-4 score74.24—Unverified
2Qwen2-VL-72BGPT-4 score74—Unverified
3InternVL2.5-78BGPT-4 score72.3—Unverified
4GPT-4o +text rationale +IoTGPT-4 score72.2—Unverified
5Lyra-ProGPT-4 score71.4—Unverified
6GLM-4V-PlusGPT-4 score71.1—Unverified
7Phantom-7BGPT-4 score70.8—Unverified
8InternVL2.5-38BGPT-4 score68.8—Unverified
9InternVL2-26B (SGP, token ratio 64%)GPT-4 score65.6—Unverified
10Baichuan-Omni (7B)GPT-4 score65.4—Unverified