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Visual Question Answering

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Papers

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Showing 1051–1100 of 2177 papers

TitleStatusHype
LLARVA: Vision-Action Instruction Tuning Enhances Robot Learning—0
Few-Shot Image Classification and Segmentation as Visual Question Answering Using Vision-Language Models—0
FedPIA -- Permuting and Integrating Adapters leveraging Wasserstein Barycenters for Finetuning Foundation Models in Multi-Modal Federated Learning—0
LLaVA-Octopus: Unlocking Instruction-Driven Adaptive Projector Fusion for Video Understanding—0
Zero-Shot Transfer VQA Dataset—0
Feature4X: Bridging Any Monocular Video to 4D Agentic AI with Versatile Gaussian Feature Fields—0
Fast or Slow? Integrating Fast Intuition and Deliberate Thinking for Enhancing Visual Question Answering—0
FashionVQA: A Domain-Specific Visual Question Answering System—0
LLaVA-Ultra: Large Chinese Language and Vision Assistant for Ultrasound—0
Face-MLLM: A Large Face Perception Model—0
VGNMN: Video-grounded Neural Module Networks for Video-Grounded Dialogue Systems—0
Look Before You Decide: Prompting Active Deduction of MLLMs for Assumptive Reasoning—0
LMME3DHF: Benchmarking and Evaluating Multimodal 3D Human Face Generation with LMMs—0
EyeFound: A Multimodal Generalist Foundation Model for Ophthalmic Imaging—0
Localize, Group, and Select: Boosting Text-VQA by Scene Text Modeling—0
VGNMN: Video-grounded Neural Module Network to Video-Grounded Language Tasks—0
Locate Then Generate: Bridging Vision and Language with Bounding Box for Scene-Text VQA—0
Extracting Training Data from Document-Based VQA Models—0
Achieving Human Parity on Visual Question Answering—0
Logically Consistent Loss for Visual Question Answering—0
LOIS: Looking Out of Instance Semantics for Visual Question Answering—0
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
Exploring Weaknesses of VQA Models through Attribution Driven Insights—0
Look, Read and Ask: Learning to Ask Questions by Reading Text in Images—0
When are Lemons Purple? The Concept Association Bias of Vision-Language Models—0
Accuracy vs. Complexity: A Trade-off in Visual Question Answering Models—0
Exploring the Frontier of Vision-Language Models: A Survey of Current Methodologies and Future Directions—0
An Empirical Study on Leveraging Scene Graphs for Visual Question Answering—0
LRRA:A Transparent Neural-Symbolic Reasoning Framework for Real-World Visual Question Answering—0
Exploring the Effectiveness of Object-Centric Representations in Visual Question Answering: Comparative Insights with Foundation Models—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
Exploring Spatial Language Grounding Through Referring Expressions—0
Exploring Sparse Spatial Relation in Graph Inference for Text-Based VQA—0
An Empirical Study of Batch Normalization and Group Normalization in Conditional Computation—0
Exploring Question Decomposition for Zero-Shot VQA—0
Exploring Human-like Attention Supervision in Visual Question Answering—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
Exploring Diverse Methods in Visual Question Answering—0
Exploring Advanced Techniques for Visual Question Answering: A Comprehensive Comparison—0
Making the Most of What You Have: Adapting Pre-trained Visual Language Models in the Low-data Regime—0
An Empirical Evaluation of Visual Question Answering for Novel Objects—0
Explore the Hallucination on Low-level Perception for MLLMs—0
Video Question Answering for People with Visual Impairments Using an Egocentric 360-Degree Camera—0
MAMO: Masked Multimodal Modeling for Fine-Grained Vision-Language Representation Learning—0
Explicit Reasoning over End-to-End Neural Architectures for Visual Question Answering—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