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Visual Question Answering

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TitleStatusHype
A Novel Framework for Robustness Analysis of Visual QA Models—0
High-Order Attention Models for Visual Question AnsweringCode0
Active Learning for Visual Question Answering: An Empirical StudyCode0
iVQA: Inverse Visual Question Answering—0
Fooling Vision and Language Models Despite Localization and Attention Mechanism—0
Survey of Recent Advances in Visual Question Answering—0
Visual Reference Resolution using Attention Memory for Visual Dialog—0
Visual Question Generation as Dual Task of Visual Question Answering—0
Exploring Human-like Attention Supervision in Visual Question Answering—0
Robustness Analysis of Visual QA Models by Basic Questions—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