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Visual Question Answering

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Showing 1251–1275 of 2177 papers

TitleStatusHype
Looking Beyond Text: Reducing Language bias in Large Vision-Language Models via Multimodal Dual-Attention and Soft-Image Guidance—0
Look, Learn and Leverage (L^3): Mitigating Visual-Domain Shift and Discovering Intrinsic Relations via Symbolic Alignment—0
Look, Read and Ask: Learning to Ask Questions by Reading Text in Images—0
LRRA:A Transparent Neural-Symbolic Reasoning Framework for Real-World Visual Question Answering—0
Can You Explain That? Lucid Explanations Help Human-AI Collaborative Image Retrieval—0
LVLM_CSP: Accelerating Large Vision Language Models via Clustering, Scattering, and Pruning for Reasoning Segmentation—0
M3DocRAG: Multi-modal Retrieval is What You Need for Multi-page Multi-document Understanding—0
M4CXR: Exploring Multi-task Potentials of Multi-modal Large Language Models for Chest X-ray Interpretation—0
MagiC: Evaluating Multimodal Cognition Toward Grounded Visual Reasoning—0
MAGIC-VQA: Multimodal And Grounded Inference with Commonsense Knowledge for Visual Question Answering—0
Making the Most of What You Have: Adapting Pre-trained Visual Language Models in the Low-data Regime—0
MAMO: Masked Multimodal Modeling for Fine-Grained Vision-Language Representation Learning—0
MANGO: Enhancing the Robustness of VQA Models via Adversarial Noise Generation—0
Mask4Align: Aligned Entity Prompting with Color Masks for Multi-Entity Localization Problems—0
MatCha: Enhancing Visual Language Pretraining with Math Reasoning and Chart Derendering—0
MaVEn: An Effective Multi-granularity Hybrid Visual Encoding Framework for Multimodal Large Language Model—0
Measuring CLEVRness: Black-box Testing of Visual Reasoning Models—0
Measuring CLEVRness: Blackbox testing of Visual Reasoning Models—0
Measuring Machine Intelligence Through Visual Question Answering—0
Med-2E3: A 2D-Enhanced 3D Medical Multimodal Large Language Model—0
Medical Visual Question Answering: A Survey—0
Medical visual question answering using joint self-supervised learning—0
MedOrch: Medical Diagnosis with Tool-Augmented Reasoning Agents for Flexible Extensibility—0
MedThink: Explaining Medical Visual Question Answering via Multimodal Decision-Making Rationale—0
MedVLM-R1: Incentivizing Medical Reasoning Capability of Vision-Language Models (VLMs) via Reinforcement Learning—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