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Visual Question Answering

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Papers

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Showing 1076–1100 of 2177 papers

TitleStatusHype
Benchmarking Vision Language Models for Cultural Understanding—0
Segmentation-guided Attention for Visual Question Answering from Remote Sensing Images—0
Extracting Training Data from Document-Based VQA Models—0
VQA-Diff: Exploiting VQA and Diffusion for Zero-Shot Image-to-3D Vehicle Asset Generation in Autonomous Driving—0
Large Language Models Understand LayoutCode0
Rethinking Visual Prompting for Multimodal Large Language Models with External Knowledge—0
Second Place Solution of WSDM2023 Toloka Visual Question Answering Challenge—0
Black-box Model Ensembling for Textual and Visual Question Answering via Information FusionCode0
BACON: Improving Clarity of Image Captions via Bag-of-Concept Graphs—0
MindBench: A Comprehensive Benchmark for Mind Map Structure Recognition and Analysis—0
InternLM-XComposer-2.5: A Versatile Large Vision Language Model Supporting Long-Contextual Input and Output—0
Visual Robustness Benchmark for Visual Question Answering (VQA)Code0
Certainly Uncertain: A Benchmark and Metric for Multimodal Epistemic and Aleatoric Awareness—0
Assistive Image Annotation Systems with Deep Learning and Natural Language Capabilities: A Review—0
The Illusion of Competence: Evaluating the Effect of Explanations on Users' Mental Models of Visual Question Answering SystemsCode0
FlowVQA: Mapping Multimodal Logic in Visual Question Answering with Flowcharts—0
Disentangling Knowledge-based and Visual Reasoning by Question Decomposition in KB-VQA—0
Enhancing Continual Learning in Visual Question Answering with Modality-Aware Feature DistillationCode0
Evaluating Fairness in Large Vision-Language Models Across Diverse Demographic Attributes and PromptsCode0
Claude 3.5 Sonnet Model Card Addendum—0
GPT-4V Explorations: Mining Autonomous Driving—0
MM-SpuBench: Towards Better Understanding of Spurious Biases in Multimodal LLMs—0
MR-MLLM: Mutual Reinforcement of Multimodal Comprehension and Vision Perception—0
Tri-VQA: Triangular Reasoning Medical Visual Question Answering for Multi-Attribute Analysis—0
Does Object Grounding Really Reduce Hallucination of Large Vision-Language 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