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Visual Question Answering

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Showing 2151–2177 of 2177 papers

TitleStatusHype
Learning to Compress Contexts for Efficient Knowledge-based Visual Question Answering—0
Fine-tuning vs From Scratch: Do Vision & Language Models Have Similar Capabilities on Out-of-Distribution Visual Question Answering?—0
An experimental study of the vision-bottleneck in VQA—0
Learning to Disambiguate by Asking Discriminative Questions—0
Correlation Information Bottleneck: Towards Adapting Pretrained Multimodal Models for Robust Visual Question Answering—0
VDocRAG: Retrieval-Augmented Generation over Visually-Rich Documents—0
V-Doc : Visual questions answers with Documents—0
V-Doc: Visual Questions Answers With Documents—0
An Evaluation of GPT-4V and Gemini in Online VQA—0
Neural Reasoning, Fast and Slow, for Video Question Answering—0
Learning to Recognize the Unseen Visual Predicates—0
Learning to Select Question-Relevant Relations for Visual Question Answering—0
Learning to Specialize with Knowledge Distillation for Visual Question Answering—0
Fine-tuning Large Language Models with Sequential Instructions—0
Learning Visual Knowledge Memory Networks for Visual Question Answering—0
An Empirical Study on the Language Modal in Visual Question Answering—0
Learning What Makes a Difference from Counterfactual Examples and Gradient Supervision—0
Lego: Learning to Disentangle and Invert Personalized Concepts Beyond Object Appearance in Text-to-Image Diffusion Models—0
LEGO-Puzzles: How Good Are MLLMs at Multi-Step Spatial Reasoning?—0
Fine-Grained Retrieval-Augmented Generation for Visual Question Answering—0
Less Is More: Linear Layers on CLIP Features as Powerful VizWiz Model—0
Let's ViCE! Mimicking Human Cognitive Behavior in Image Generation Evaluation—0
Leveraging Medical Visual Question Answering with Supporting Facts—0
Leveraging Visual Question Answering for Image-Caption Ranking—0
Leveraging Visual Question Answering to Improve Text-to-Image Synthesis—0
Light as Deception: GPT-driven Natural Relighting Against Vision-Language Pre-training Models—0
Lightweight In-Context Tuning for Multimodal Unified Models—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