SOTAVerified

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 551–600 of 2167 papers

TitleStatusHype
Enhanced Textual Feature Extraction for Visual Question Answering: A Simple Convolutional Approach—0
Visual Fact Checker: Enabling High-Fidelity Detailed Caption Generation—0
Multi-Page Document Visual Question Answering using Self-Attention Scoring MechanismCode0
ViOCRVQA: Novel Benchmark Dataset and Vision Reader for Visual Question Answering by Understanding Vietnamese Text in ImagesCode1
NTIRE 2024 Quality Assessment of AI-Generated Content Challenge—0
RadGenome-Chest CT: A Grounded Vision-Language Dataset for Chest CT AnalysisCode0
AIS 2024 Challenge on Video Quality Assessment of User-Generated Content: Methods and ResultsCode0
Fusion of Domain-Adapted Vision and Language Models for Medical Visual Question Answering—0
MDAgents: An Adaptive Collaboration of LLMs for Medical Decision-MakingCode3
Self-Bootstrapped Visual-Language Model for Knowledge Selection and Question AnsweringCode0
Exploring Diverse Methods in Visual Question Answering—0
TextSquare: Scaling up Text-Centric Visual Instruction Tuning—0
PDF-MVQA: A Dataset for Multimodal Information Retrieval in PDF-based Visual Question Answering—0
Unified Scene Representation and Reconstruction for 3D Large Language Models—0
Look Before You Decide: Prompting Active Deduction of MLLMs for Assumptive Reasoning—0
LaPA: Latent Prompt Assist Model For Medical Visual Question AnsweringCode1
MedThink: Explaining Medical Visual Question Answering via Multimodal Decision-Making Rationale—0
Reka Core, Flash, and Edge: A Series of Powerful Multimodal Language Models—0
NTIRE 2024 Challenge on Short-form UGC Video Quality Assessment: Methods and ResultsCode2
Med-MoE: Mixture of Domain-Specific Experts for Lightweight Medical Vision-Language ModelsCode2
ViTextVQA: A Large-Scale Visual Question Answering Dataset for Evaluating Vietnamese Text Comprehension in ImagesCode0
Find The Gap: Knowledge Base Reasoning For Visual Question Answering—0
TextCoT: Zoom In for Enhanced Multimodal Text-Rich Image UnderstandingCode1
Enhancing Visual Question Answering through Question-Driven Image Captions as PromptsCode1
Learning to Localize Objects Improves Spatial Reasoning in Visual-LLMsCode0
BRAVE: Broadening the visual encoding of vision-language models—0
OmniFusion Technical ReportCode0
MA-LMM: Memory-Augmented Large Multimodal Model for Long-Term Video UnderstandingCode3
HAMMR: HierArchical MultiModal React agents for generic VQA—0
Study of the effect of Sharpness on Blind Video Quality Assessment—0
Joint Visual and Text Prompting for Improved Object-Centric Perception with Multimodal Large Language ModelsCode0
BuDDIE: A Business Document Dataset for Multi-task Information Extraction—0
TinyVQA: Compact Multimodal Deep Neural Network for Visual Question Answering on Resource-Constrained Devices—0
Detect2Interact: Localizing Object Key Field in Visual Question Answering (VQA) with LLMs—0
Evaluating Text-to-Visual Generation with Image-to-Text GenerationCode3
Design as Desired: Utilizing Visual Question Answering for Multimodal Pre-trainingCode0
Unsolvable Problem Detection: Evaluating Trustworthiness of Vision Language ModelsCode2
JDocQA: Japanese Document Question Answering Dataset for Generative Language ModelsCode1
Quantifying and Mitigating Unimodal Biases in Multimodal Large Language Models: A Causal PerspectiveCode1
Visual Hallucination: Definition, Quantification, and Prescriptive Remediations—0
A Gaze-grounded Visual Question Answering Dataset for Clarifying Ambiguous Japanese Questions—0
Intrinsic Subgraph Generation for Interpretable Graph based Visual Question AnsweringCode0
Visual CoT: Advancing Multi-Modal Language Models with a Comprehensive Dataset and Benchmark for Chain-of-Thought ReasoningCode3
Synthesize Step-by-Step: Tools, Templates and LLMs as Data Generators for Reasoning-Based Chart VQA—0
IllusionVQA: A Challenging Optical Illusion Dataset for Vision Language ModelsCode1
Surgical-LVLM: Learning to Adapt Large Vision-Language Model for Grounded Visual Question Answering in Robotic Surgery—0
MedPromptX: Grounded Multimodal Prompting for Chest X-ray DiagnosisCode2
Multi-Agent VQA: Exploring Multi-Agent Foundation Models in Zero-Shot Visual Question AnsweringCode1
Multi-Modal Hallucination Control by Visual Information Grounding—0
vid-TLDR: Training Free Token merging for Light-weight Video TransformerCode2
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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