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

Showing 9511000 of 2167 papers

TitleStatusHype
Multimodal Explanations: Justifying Decisions and Pointing to the EvidenceCode0
MHSAN: Multi-Head Self-Attention Network for Visual Semantic EmbeddingCode0
Enhancing Vietnamese VQA through Curriculum Learning on Raw and Augmented Text RepresentationsCode0
Enhancing the AI2 Diagrams Dataset Using Rhetorical Structure TheoryCode0
Medical Large Vision Language Models with Multi-Image Visual AbilityCode0
Med-PMC: Medical Personalized Multi-modal Consultation with a Proactive Ask-First-Observe-Next ParadigmCode0
Mimic and Fool: A Task Agnostic Adversarial AttackCode0
Measuring Faithful and Plausible Visual Grounding in VQACode0
Enhancing Continual Learning in Visual Question Answering with Modality-Aware Feature DistillationCode0
μ-Bench: A Vision-Language Benchmark for Microscopy UnderstandingCode0
Answer Them All! Toward Universal Visual Question Answering ModelsCode0
MedHallTune: An Instruction-Tuning Benchmark for Mitigating Medical Hallucination in Vision-Language ModelsCode0
End-to-end optimization of goal-driven and visually grounded dialogue systemsCode0
End-to-End Instance Segmentation with Recurrent AttentionCode0
Answer Questions with Right Image Regions: A Visual Attention Regularization ApproachCode0
End-to-End Audio Visual Scene-Aware Dialog using Multimodal Attention-Based Video FeaturesCode0
Adversarial Training with OCR Modality Perturbation for Scene-Text Visual Question AnsweringCode0
MaMMUT: A Simple Architecture for Joint Learning for MultiModal TasksCode0
M^2ConceptBase: A Fine-Grained Aligned Concept-Centric Multimodal Knowledge BaseCode0
Visual Contexts Clarify Ambiguous Expressions: A Benchmark DatasetCode0
ELIP: Efficient Language-Image Pre-training with Fewer Vision TokensCode0
Answering Questions about Data Visualizations using Efficient Bimodal FusionCode0
LXMERT Model Compression for Visual Question AnsweringCode0
LayoutLMv3: Pre-training for Document AI with Unified Text and Image MaskingCode0
Making the V in VQA Matter: Elevating the Role of Image Understanding in Visual Question AnsweringCode0
Marten: Visual Question Answering with Mask Generation for Multi-modal Document UnderstandingCode0
Logical Implications for Visual Question Answering ConsistencyCode0
Locally Smoothed Neural NetworksCode0
Effective Approaches to Batch Parallelization for Dynamic Neural Network ArchitecturesCode0
Looking Beyond Visible Cues: Implicit Video Question Answering via Dual-Clue ReasoningCode0
Visuo-Linguistic Question Answering (VLQA) ChallengeCode0
Visual Question Answering: A Survey of Methods and DatasetsCode0
LLM-Assisted Multi-Teacher Continual Learning for Visual Question Answering in Robotic SurgeryCode0
Learning content and context with language bias for Visual Question AnsweringCode0
Learning Convolutional Text Representations for Visual Question AnsweringCode0
Bridging Languages through Images with Deep Partial Canonical Correlation AnalysisCode0
ECG Heartbeat Classification: A Deep Transferable RepresentationCode0
Bridge the Gap Between VQA and Human Behavior on Omnidirectional Video: A Large-Scale Dataset and a Deep Learning ModelCode0
BioD2C: A Dual-level Semantic Consistency Constraint Framework for Biomedical VQACode0
Learning Representations of Sets through Optimized PermutationsCode0
LMM-VQA: Advancing Video Quality Assessment with Large Multimodal ModelsCode0
Loss re-scaling VQA: Revisiting the LanguagePrior Problem from a Class-imbalance ViewCode0
EaSe: A Diagnostic Tool for VQA based on Answer DiversityCode0
Dynamic Task and Weight Prioritization Curriculum Learning for Multimodal ImageryCode0
LININ: Logic Integrated Neural Inference Network for Explanatory Visual Question AnsweringCode0
Dynamic Memory Networks for Visual and Textual Question AnsweringCode0
Targeted Visual Prompting for Medical Visual Question AnsweringCode0
LLaVA-OneVision: Easy Visual Task TransferCode0
Learning to Collocate Visual-Linguistic Neural Modules for Image CaptioningCode0
LPF: A Language-Prior Feedback Objective Function for De-biased Visual Question AnsweringCode0
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Benchmark Results

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