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 18511900 of 2167 papers

TitleStatusHype
Grounded Word Sense Translation0
ImageTTR: Grounding Type Theory with Records in Image Classification for Visual Question Answering0
Viewport Proposal CNN for 360deg Video Quality AssessmentCode0
Dynamic Fusion With Intra- and Inter-Modality Attention Flow for Visual Question Answering0
OK-VQA: A Visual Question Answering Benchmark Requiring External KnowledgeCode1
Scene Text Visual Question AnsweringCode1
What Can Neural Networks Reason About?Code0
Vision-to-Language Tasks Based on Attributes and Attention Mechanism0
Leveraging Medical Visual Question Answering with Supporting Facts0
Structure Learning for Neural Module Networks0
Why do These Match? Explaining the Behavior of Image Similarity ModelsCode0
Self-Critical Reasoning for Robust Visual Question AnsweringCode0
Aligning Visual Regions and Textual Concepts for Semantic-Grounded Image RepresentationsCode0
Misleading Failures of Partial-input Baselines0
Quantifying and Alleviating the Language Prior Problem in Visual Question AnsweringCode0
Language-Conditioned Graph Networks for Relational ReasoningCode0
Visual TTR - Modelling Visual Question Answering in Type Theory with Records0
Routing Networks and the Challenges of Modular and Compositional ComputationCode0
The Neuro-Symbolic Concept Learner: Interpreting Scenes, Words, and Sentences From Natural SupervisionCode0
Scene Graph Prediction with Limited LabelsCode0
Progressive Attention Memory Network for Movie Story Question Answering0
Learning to Collocate Neural Modules for Image Captioning0
Towards VQA Models That Can ReadCode3
Question Guided Modular Routing Networks for Visual Question Answering0
Evaluating the Representational Hub of Language and Vision Models0
Factor Graph AttentionCode0
Text Guided Person Image Synthesis0
Multi-Target Embodied Question AnsweringCode0
Heterogeneous Memory Enhanced Multimodal Attention Model for Video Question AnsweringCode0
Actively Seeking and Learning from Live Data0
Can You Explain That? Lucid Explanations Help Human-AI Collaborative Image Retrieval0
MMED: A Multi-domain and Multi-modality Event Dataset0
Relation-Aware Graph Attention Network for Visual Question AnsweringCode0
Visual Query Answering by Entity-Attribute Graph Matching and Reasoning0
RAVEN: A Dataset for Relational and Analogical Visual rEasoNing0
Answer Them All! Toward Universal Visual Question Answering ModelsCode0
MUREL: Multimodal Relational Reasoning for Visual Question AnsweringCode0
GQA: A New Dataset for Real-World Visual Reasoning and Compositional Question AnsweringCode1
Dual Attention Networks for Visual Reference Resolution in Visual DialogCode0
Probabilistic Neural-symbolic Models for Interpretable Visual Question Answering0
Generating Natural Language Explanations for Visual Question Answering using Scene Graphs and Visual Attention0
Cycle-Consistency for Robust Visual Question Answering0
Taking a HINT: Leveraging Explanations to Make Vision and Language Models More Grounded0
VrR-VG: Refocusing Visually-Relevant Relationships0
BLOCK: Bilinear Superdiagonal Fusion for Visual Question Answering and Visual Relationship DetectionCode0
Visual Entailment: A Novel Task for Fine-Grained Image Understanding0
CLEVR-Ref+: Diagnosing Visual Reasoning with Referring ExpressionsCode0
The meaning of "most" for visual question answering models0
Scene Graph Reasoning with Prior Visual Relationship for Visual Question Answering0
Focal Visual-Text Attention for Memex 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
9InternVL-CAccuracy81.2Unverified
10LyricsAccuracy81.2Unverified
#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