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 701750 of 2167 papers

TitleStatusHype
Noise Estimation Using Density Estimation for Self-Supervised Multimodal LearningCode0
Adaptively Clustering Neighbor Elements for Image-Text GenerationCode0
Aligning Visual Regions and Textual Concepts for Semantic-Grounded Image RepresentationsCode0
No Images, No Problem: Retaining Knowledge in Continual VQA with Questions-Only MemoryCode0
Noise-Induced Barren Plateaus in Variational Quantum AlgorithmsCode0
Neural Module NetworksCode0
NeSyCoCo: A Neuro-Symbolic Concept Composer for Compositional GeneralizationCode0
Neural-Symbolic VQA: Disentangling Reasoning from Vision and Language UnderstandingCode0
Composition Vision-Language Understanding via Segment and Depth Anything ModelCode0
Navigating Cultural Chasms: Exploring and Unlocking the Cultural POV of Text-To-Image ModelsCode0
NAAQA: A Neural Architecture for Acoustic Question AnsweringCode0
MUREL: Multimodal Relational Reasoning for Visual Question AnsweringCode0
Multi-Target Embodied Question AnsweringCode0
MUTAN: Multimodal Tucker Fusion for Visual Question AnsweringCode0
Multiscale Byte Language Models -- A Hierarchical Architecture for Causal Million-Length Sequence ModelingCode0
Grad-CAM: Why did you say that?Code0
Multi-Sourced Compositional Generalization in Visual Question AnsweringCode0
No-Reference Video Quality Assessment Based on Benford’s Law and Perceptual FeaturesCode0
GradBias: Unveiling Word Influence on Bias in Text-to-Image Generative ModelsCode0
Compact Trilinear Interaction for Visual Question AnsweringCode0
Multimodal Residual Learning for Visual QACode0
CommVQA: Situating Visual Question Answering in Communicative ContextsCode0
Multimodal Hypothetical Summary for Retrieval-based Multi-image Question AnsweringCode0
Multimodal Compact Bilinear Pooling for Visual Question Answering and Visual GroundingCode0
Multimodal Explanations: Justifying Decisions and Pointing to the EvidenceCode0
Multi-modal Factorized Bilinear Pooling with Co-Attention Learning for Visual Question AnsweringCode0
Multi-Page Document Visual Question Answering using Self-Attention Scoring MechanismCode0
Multi-Image Visual Question AnsweringCode0
COLUMBUS: Evaluating COgnitive Lateral Understanding through Multiple-choice reBUSesCode0
Adapting Lightweight Vision Language Models for Radiological Visual Question AnsweringCode0
Generalizing Visual Question Answering from Synthetic to Human-Written Questions via a Chain of QA with a Large Language ModelCode0
MQA: Answering the Question via Robotic ManipulationCode0
General Greedy De-bias LearningCode0
Cognitive Visual Commonsense Reasoning Using Dynamic Working MemoryCode0
Multiple interaction learning with question-type prior knowledge for constraining answer search space in visual question answeringCode0
No-Reference Video Quality Assessment Using Space-Time ChipsCode0
Modeling Relationships in Referential Expressions with Compositional Modular NetworksCode0
Ask Your Neurons: A Deep Learning Approach to Visual Question AnsweringCode0
Compositionality as Lexical SymmetryCode0
Modularized Zero-shot VQA with Pre-trained ModelsCode0
Multiview Contrastive Learning for Completely Blind Video Quality Assessment of User Generated ContentCode0
GAMIVAL: Video Quality Prediction on Mobile Cloud Gaming ContentCode0
Game of Sketches: Deep Recurrent Models of Pictionary-style Word GuessingCode0
FVQ: A Large-Scale Dataset and A LMM-based Method for Face Video Quality AssessmentCode0
Co-attending Regions and Detections with Multi-modal Multiplicative Embedding for VQACode0
Co-attending Free-form Regions and Detections with Multi-modal Multiplicative Feature Embedding for Visual Question AnsweringCode0
Compressing And Debiasing Vision-Language Pre-Trained Models for Visual Question AnsweringCode0
A Joint Sequence Fusion Model for Video Question Answering and RetrievalCode0
Adaptive loose optimization for robust question answeringCode0
Modulating early visual processing by languageCode0
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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