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 17511800 of 2167 papers

TitleStatusHype
Unleashing the Potentials of Likelihood Composition for Multi-modal Language ModelsCode0
MUTAN: Multimodal Tucker Fusion for Visual Question AnsweringCode0
MQA: Answering the Question via Robotic ManipulationCode0
Unmasked Teacher: Towards Training-Efficient Video Foundation ModelsCode0
NAAQA: A Neural Architecture for Acoustic Question AnsweringCode0
Active Learning for Visual Question Answering: An Empirical StudyCode0
Modulating early visual processing by languageCode0
StarVQA+: Co-training Space-Time Attention for Video Quality AssessmentCode0
StarVQA: Space-Time Attention for Video Quality AssessmentCode0
Navigating Cultural Chasms: Exploring and Unlocking the Cultural POV of Text-To-Image ModelsCode0
VTQA: Visual Text Question Answering via Entity Alignment and Cross-Media ReasoningCode0
End-to-End Audio Visual Scene-Aware Dialog using Multimodal Attention-Based Video FeaturesCode0
Modularized Zero-shot VQA with Pre-trained ModelsCode0
NeSyCoCo: A Neuro-Symbolic Concept Composer for Compositional GeneralizationCode0
ELIP: Efficient Language-Image Pre-training with Fewer Vision TokensCode0
Co-attending Free-form Regions and Detections with Multi-modal Multiplicative Feature Embedding for Visual Question AnsweringCode0
Neural Module NetworksCode0
Effective Approaches to Batch Parallelization for Dynamic Neural Network ArchitecturesCode0
Neural-Symbolic VQA: Disentangling Reasoning from Vision and Language UnderstandingCode0
CLIPVQA:Video Quality Assessment via CLIPCode0
A Joint Sequence Fusion Model for Video Question Answering and RetrievalCode0
Structured Attentions for Visual Question AnsweringCode0
ECG Heartbeat Classification: A Deep Transferable RepresentationCode0
Structured Triplet Learning with POS-tag Guided Attention for Visual Question AnsweringCode0
Visual Question Answering using Deep Learning: A Survey and Performance AnalysisCode0
Modeling Relationships in Referential Expressions with Compositional Modular NetworksCode0
MIRTT: Learning Multimodal Interaction Representations from Trilinear Transformers for Visual Question AnsweringCode0
Mimic and Fool: A Task Agnostic Adversarial AttackCode0
MHSAN: Multi-Head Self-Attention Network for Visual Semantic EmbeddingCode0
AdCare-VLM: Leveraging Large Vision Language Model (LVLM) to Monitor Long-Term Medication Adherence and CareCode0
No Images, No Problem: Retaining Knowledge in Continual VQA with Questions-Only MemoryCode0
Noise Estimation Using Density Estimation for Self-Supervised Multimodal LearningCode0
Noise-Induced Barren Plateaus in Variational Quantum AlgorithmsCode0
Watch Out Your Album! On the Inadvertent Privacy Memorization in Multi-Modal Large Language ModelsCode0
No-Reference Video Quality Assessment Based on Benford’s Law and Perceptual FeaturesCode0
ClinKD: Cross-Modal Clinical Knowledge Distiller For Multi-Task Medical ImagesCode0
No-Reference Video Quality Assessment Using Space-Time ChipsCode0
Study on the Assessment of the Quality of Experience of Streaming VideoCode0
Subjective and Objective Analysis of Indian Social Media Video QualityCode0
USER-VLM 360: Personalized Vision Language Models with User-aware Tuning for Social Human-Robot InteractionsCode0
Med-PMC: Medical Personalized Multi-modal Consultation with a Proactive Ask-First-Observe-Next ParadigmCode0
Subjective and Objective Audio-Visual Quality Assessment for User Generated ContentCode0
Subjective and Objective Quality Assessment of High-Motion Sports Videos at Low-BitratesCode0
Medical Large Vision Language Models with Multi-Image Visual AbilityCode0
Visual Question Answering: which investigated applications?Code0
MedHallTune: An Instruction-Tuning Benchmark for Mitigating Medical Hallucination in Vision-Language ModelsCode0
NTIRE 2025 Challenge on Video Quality Enhancement for Video Conferencing: Datasets, Methods and ResultsCode0
Measuring Faithful and Plausible Visual Grounding in VQACode0
μ-Bench: A Vision-Language Benchmark for Microscopy UnderstandingCode0
Marten: Visual Question Answering with Mask Generation for Multi-modal Document UnderstandingCode0
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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