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Visual Question Answering (VQA)

Visual Question Answering (VQA) is a task in computer vision that involves answering questions about an image. The goal of VQA is to teach machines to understand the content of an image and answer questions about it in natural language.

Image Source: visualqa.org

Papers

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Showing 401–450 of 2167 papers

TitleStatusHype
@Bench: Benchmarking Vision-Language Models for Human-centered Assistive Technology—0
JourneyBench: A Challenging One-Stop Vision-Language Understanding Benchmark of Generated ImagesCode0
Evaluating Image Hallucination in Text-to-Image Generation with Question-AnsweringCode1
Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any ResolutionCode11
Sparks of Artificial General Intelligence(AGI) in Semiconductor Material Science: Early Explorations into the Next Frontier of Generative AI-Assisted Electron Micrograph Analysis—0
Less is More: A Simple yet Effective Token Reduction Method for Efficient Multi-modal LLMsCode1
CAST: Cross-modal Alignment Similarity Test for Vision Language ModelsCode0
Guiding Vision-Language Model Selection for Visual Question-Answering Across Tasks, Domains, and Knowledge TypesCode0
QTG-VQA: Question-Type-Guided Architectural for VideoQA Systems—0
Learning to Compress Contexts for Efficient Knowledge-based Visual Question Answering—0
COLUMBUS: Evaluating COgnitive Lateral Understanding through Multiple-choice reBUSesCode0
How to Determine the Preferred Image Distribution of a Black-Box Vision-Language Model?Code0
Kvasir-VQA: A Text-Image Pair GI Tract DatasetCode0
Look, Learn and Leverage (L^3): Mitigating Visual-Domain Shift and Discovering Intrinsic Relations via Symbolic Alignment—0
Retrieval-Augmented Natural Language Reasoning for Explainable Visual Question Answering—0
M4CXR: Exploring Multi-task Potentials of Multi-modal Large Language Models for Chest X-ray Interpretation—0
GradBias: Unveiling Word Influence on Bias in Text-to-Image Generative ModelsCode0
Can Visual Language Models Replace OCR-Based Visual Question Answering Pipelines in Production? A Case Study in Retail—0
Can SAR improve RSVQA performance?—0
Multi-Modal Instruction-Tuning Small-Scale Language-and-Vision Assistant for Semiconductor Electron Micrograph Analysis—0
Zero-Shot Visual Reasoning by Vision-Language Models: Benchmarking and Analysis—0
LMM-VQA: Advancing Video Quality Assessment with Large Multimodal ModelsCode0
Towards Human-Level Understanding of Complex Process Engineering Schematics: A Pedagogical, Introspective Multi-Agent Framework for Open-Domain Question Answering—0
Foundational Model for Electron Micrograph Analysis: Instruction-Tuning Small-Scale Language-and-Vision Assistant for Enterprise Adoption—0
Vintern-1B: An Efficient Multimodal Large Language Model for Vietnamese—0
AIM 2024 Challenge on Compressed Video Quality Assessment: Methods and ResultsCode1
Swarm Intelligence in Geo-Localization: A Multi-Agent Large Vision-Language Model Collaborative Framework—0
VE-Bench: Subjective-Aligned Benchmark Suite for Text-Driven Video Editing Quality AssessmentCode2
V-RoAst: Visual Road Assessment. Can VLM be a Road Safety Assessor Using the iRAP Standard?Code1
FFAA: Multimodal Large Language Model based Explainable Open-World Face Forgery Analysis AssistantCode1
PA-LLaVA: A Large Language-Vision Assistant for Human Pathology Image UnderstandingCode2
Beyond the Hype: A dispassionate look at vision-language models in medical scenario—0
Visual Agents as Fast and Slow ThinkersCode1
Med-PMC: Medical Personalized Multi-modal Consultation with a Proactive Ask-First-Observe-Next ParadigmCode0
Enhancing Visual Question Answering through Ranking-Based Hybrid Training and Multimodal Fusion—0
Subjective and Objective Quality Assessment of Rendered Human Avatar Videos in Virtual Reality—0
Long-Form Answers to Visual Questions from Blind and Low Vision People—0
SWIFT:A Scalable lightWeight Infrastructure for Fine-TuningCode11
Efficient Quantum Gradient and Higher-order Derivative Estimation via Generalized Hadamard Test—0
Surgical-VQLA++: Adversarial Contrastive Learning for Calibrated Robust Visual Question-Localized Answering in Robotic SurgeryCode1
mPLUG-Owl3: Towards Long Image-Sequence Understanding in Multi-Modal Large Language ModelsCode7
Revisiting Multi-Modal LLM Evaluation—0
GMAI-MMBench: A Comprehensive Multimodal Evaluation Benchmark Towards General Medical AICode2
LLaVA-OneVision: Easy Visual Task TransferCode0
Targeted Visual Prompting for Medical Visual Question AnsweringCode0
MedTrinity-25M: A Large-scale Multimodal Dataset with Multigranular Annotations for MedicineCode3
Towards Flexible Evaluation for Generative Visual Question AnsweringCode0
Benchmarking Multi-dimensional AIGC Video Quality Assessment: A Dataset and Unified ModelCode0
SimpleLLM4AD: An End-to-End Vision-Language Model with Graph Visual Question Answering for Autonomous Driving—0
Prompting Medical Large Vision-Language Models to Diagnose Pathologies by Visual Question Answering—0
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All datasetsGQA Test2019VQA v2 test-devVQA v2 test-stdOK-VQAMSVD-QADocVQA testMSRVTT-QAInfographicVQAGQA test-devVizWiz 2020 VQAA-OKVQACLEVR

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1humanAccuracy89.3—Unverified
2DREAM+Unicoder-VL (MSRA)Accuracy76.04—Unverified
3TRRNet (Ensemble)Accuracy74.03—Unverified
4MIL-nbgaoAccuracy73.81—Unverified
5Kakao BrainAccuracy73.33—Unverified
6Coarse-to-Fine Reasoning, Single ModelAccuracy72.14—Unverified
7270Accuracy70.23—Unverified
8NSM ensemble (updated)Accuracy67.55—Unverified
9VinVL-DPTAccuracy64.92—Unverified
10VinVL+LAccuracy64.85—Unverified
#ModelMetricClaimedVerifiedStatus
1PaLIAccuracy84.3—Unverified
2BEiT-3Accuracy84.19—Unverified
3VLMoAccuracy82.78—Unverified
4ONE-PEACEAccuracy82.6—Unverified
5mPLUG (Huge)Accuracy82.43—Unverified
6CuMo-7BAccuracy82.2—Unverified
7X2-VLM (large)Accuracy81.9—Unverified
8MMUAccuracy81.26—Unverified
9InternVL-CAccuracy81.2—Unverified
10LyricsAccuracy81.2—Unverified
#ModelMetricClaimedVerifiedStatus
1BEiT-3overall84.03—Unverified
2mPLUG-Hugeoverall83.62—Unverified
3ONE-PEACEoverall82.52—Unverified
4X2-VLM (large)overall81.8—Unverified
5VLMooverall81.3—Unverified
6SimVLMoverall80.34—Unverified
7X2-VLM (base)overall80.2—Unverified
8VASToverall80.19—Unverified
9VALORoverall78.62—Unverified
10Prompt Tuningoverall78.53—Unverified