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 401450 of 2167 papers

TitleStatusHype
Reliable Visual Question Answering: Abstain Rather Than Answer IncorrectlyCode1
GRIT: General Robust Image Task BenchmarkCode1
RelViT: Concept-guided Vision Transformer for Visual Relational ReasoningCode1
Hypergraph Transformer: Weakly-supervised Multi-hop Reasoning for Knowledge-based Visual Question AnsweringCode1
Attention in Reasoning: Dataset, Analysis, and ModelingCode1
SwapMix: Diagnosing and Regularizing the Over-Reliance on Visual Context in Visual Question AnsweringCode1
CLEVR-X: A Visual Reasoning Dataset for Natural Language ExplanationsCode1
End-to-end Document Recognition and Understanding with DessurtCode1
Learning to Answer Questions in Dynamic Audio-Visual ScenariosCode1
MuKEA: Multimodal Knowledge Extraction and Accumulation for Knowledge-based Visual Question AnsweringCode1
NLX-GPT: A Model for Natural Language Explanations in Vision and Vision-Language TasksCode1
AssistQ: Affordance-centric Question-driven Task Completion for Egocentric AssistantCode1
IGLUE: A Benchmark for Transfer Learning across Modalities, Tasks, and LanguagesCode1
FAVER: Blind Quality Prediction of Variable Frame Rate VideosCode1
Maintaining Reasoning Consistency in Compositional Visual Question AnsweringCode1
LaTr: Layout-Aware Transformer for Scene-Text VQACode1
Comprehensive Visual Question Answering on Point Clouds through Compositional Scene ManipulationCode1
ScanQA: 3D Question Answering for Spatial Scene UnderstandingCode1
Align and Prompt: Video-and-Language Pre-training with Entity PromptsCode1
Distilled Dual-Encoder Model for Vision-Language UnderstandingCode1
KAT: A Knowledge Augmented Transformer for Vision-and-LanguageCode1
Bilateral Cross-Modality Graph Matching Attention for Feature Fusion in Visual Question AnsweringCode1
Dual-Key Multimodal Backdoors for Visual Question AnsweringCode1
Change Detection Meets Visual Question AnsweringCode1
Video as Conditional Graph Hierarchy for Multi-Granular Question AnsweringCode1
MLP Architectures for Vision-and-Language Modeling: An Empirical StudyCode1
Debiased Visual Question Answering from Feature and Sample PerspectivesCode1
Searching the Search Space of Vision TransformerCode1
Classification-Regression for Chart ComprehensionCode1
UniTAB: Unifying Text and Box Outputs for Grounded Vision-Language ModelingCode1
Many Heads but One Brain: Fusion Brain -- a Competition and a Single Multimodal Multitask ArchitectureCode1
Florence: A New Foundation Model for Computer VisionCode1
Multi-Grained Vision Language Pre-Training: Aligning Texts with Visual ConceptsCode1
An Empirical Study of Training End-to-End Vision-and-Language TransformersCode1
VLMo: Unified Vision-Language Pre-Training with Mixture-of-Modality-ExpertsCode1
ViVQA: Vietnamese Visual Question AnsweringCode1
Introspective Distillation for Robust Question AnsweringCode1
IconQA: A New Benchmark for Abstract Diagram Understanding and Visual Language ReasoningCode1
Label-Descriptive Patterns and Their Application to Characterizing Classification ErrorsCode1
A Good Prompt Is Worth Millions of Parameters: Low-resource Prompt-based Learning for Vision-Language ModelsCode1
Pano-AVQA: Grounded Audio-Visual Question Answering on 360^ VideosCode1
Coarse-to-Fine Reasoning for Visual Question AnsweringCode1
Counterfactual Samples Synthesizing and Training for Robust Visual Question AnsweringCode1
ProTo: Program-Guided Transformer for Program-Guided TasksCode1
The Spoon Is in the Sink: Assisting Visually Impaired People in the KitchenCode1
Calibrating Concepts and Operations: Towards Symbolic Reasoning on Real ImagesCode1
Does Vision-and-Language Pretraining Improve Lexical Grounding?Code1
ChipQA: No-Reference Video Quality Prediction via Space-Time ChipsCode1
xGQA: Cross-Lingual Visual Question AnsweringCode1
An Empirical Study of GPT-3 for Few-Shot Knowledge-Based VQACode1
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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