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 14511500 of 2167 papers

TitleStatusHype
NAAQA: A Neural Architecture for Acoustic Question AnsweringCode0
Supervising the Transfer of Reasoning Patterns in VQA0
Bayesian Attention Belief Networks0
PAM: Understanding Product Images in Cross Product Category Attribute Extraction0
Check It Again: Progressive Visual Question Answering via Visual EntailmentCode1
Are VQA Systems RAD? Measuring Robustness to Augmented Data with Focused Interventions0
Human-Adversarial Visual Question Answering0
Grounding Complex Navigational Instructions Using Scene Graphs0
Semantic Aligned Multi-modal Transformer for Vision-LanguageUnderstanding: A Preliminary Study on Visual QA0
Learning to Select Question-Relevant Relations for Visual Question Answering0
MiniVQA - A resource to build your tailored VQA competition0
CLEVR\_HYP: A Challenge Dataset and Baselines for Visual Question Answering with Hypothetical Actions over ImagesCode0
MIMOQA: Multimodal Input Multimodal Output Question Answering0
EaSe: A Diagnostic Tool for VQA based on Answer DiversityCode0
Adversarial VQA: A New Benchmark for Evaluating the Robustness of VQA Models0
LPF: A Language-Prior Feedback Objective Function for De-biased Visual Question AnsweringCode0
StructuralLM: Structural Pre-training for Form Understanding0
Multi-modal Understanding and Generation for Medical Images and Text via Vision-Language Pre-TrainingCode1
Probing Inter-modality: Visual Parsing with Self-Attention for Vision-and-Language Pre-training0
Multiple Meta-model Quantifying for Medical Visual Question AnsweringCode1
NExT-QA:Next Phase of Question-Answering to Explaining Temporal ActionsCode1
Survey of Visual-Semantic Embedding Methods for Zero-Shot Image Retrieval0
Show Why the Answer is Correct! Towards Explainable AI using Compositional Temporal Attention0
Cross-Modal Generative Augmentation for Visual Question Answering0
Found a Reason for me? Weakly-supervised Grounded Visual Question Answering using CapsulesCode1
Inter-GPS: Interpretable Geometry Problem Solving with Formal Language and Symbolic ReasoningCode1
Passage Retrieval for Outside-Knowledge Visual Question AnsweringCode1
AdaVQA: Overcoming Language Priors with Adapted Margin Cosine LossCode0
Proposal-free One-stage Referring Expression via Grid-Word Cross-Attention0
Iterated learning for emergent systematicity in VQA0
A survey on VQA_Datasets and Approaches0
Chop Chop BERT: Visual Question Answering by Chopping VisualBERT's Heads0
Optimal training of variational quantum algorithms without barren plateausCode0
Document Collection Visual Question Answering0
InfographicVQA0
MDETR -- Modulated Detection for End-to-End Multi-Modal UnderstandingCode1
RelTransformer: A Transformer-Based Long-Tail Visual Relationship RecognitionCode1
Playing Lottery Tickets with Vision and Language0
GraghVQA: Language-Guided Graph Neural Networks for Graph-based Visual Question AnsweringCode1
Cross-Modal Retrieval Augmentation for Multi-Modal Classification0
VGNMN: Video-grounded Neural Module Network to Video-Grounded Language Tasks0
Jointly Learning Truth-Conditional Denotations and Groundings using Parallel Attention0
Dealing with Missing Modalities in the Visual Question Answer-Difference Prediction Task through Knowledge Distillation0
CLEVR_HYP: A Challenge Dataset and Baselines for Visual Question Answering with Hypothetical Actions over ImagesCode0
Neuro-Symbolic VQA: A review from the perspective of AGI desiderata0
How Transferable are Reasoning Patterns in VQA?0
Multimodal Continuous Visual Attention Mechanisms0
Beyond Question-Based Biases: Assessing Multimodal Shortcut Learning in Visual Question AnsweringCode1
Compressing Visual-linguistic Model via Knowledge Distillation0
MMBERT: Multimodal BERT Pretraining for Improved Medical 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