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 19512000 of 2167 papers

TitleStatusHype
Multimodal Neural Machine Translation for Low-resource Language Pairs using Synthetic Data0
Pushing the Limits of Radiology with Joint Modeling of Visual and Textual Information0
Connecting Language and Vision to Actions0
Semantically Equivalent Adversarial Rules for Debugging NLP modelsCode0
Bridging Languages through Images with Deep Partial Canonical Correlation AnalysisCode0
End-to-End Audio Visual Scene-Aware Dialog using Multimodal Attention-Based Video FeaturesCode0
Learning Conditioned Graph Structures for Interpretable Visual Question AnsweringCode0
Learning Visual Knowledge Memory Networks for Visual Question Answering0
iParaphrasing: Extracting Visually Grounded Paraphrases via an ImageCode0
Learning Answer Embeddings for Visual Question Answering0
Cross-Dataset Adaptation for Visual Question Answering0
CS-VQA: Visual Question Answering with Compressively Sensed Images0
Focal Visual-Text Attention for Visual Question AnsweringCode0
On the Flip Side: Identifying Counterexamples in Visual Question Answering0
Categorizing Concepts With Basic Level for Vision-to-Language0
Stacked Latent Attention for Multimodal Reasoning0
An Evaluation of Image-Based Verb Prediction Models against Human Eye-Tracking Data0
Visually Guided Spatial Relation Extraction from Text0
Stacking with Auxiliary Features for Visual Question Answering0
Hyperbolic Attention Networks0
R-VQA: Learning Visual Relation Facts with Semantic Attention for Visual Question AnsweringCode1
Joint Image Captioning and Question Answering0
Bilinear Attention NetworksCode3
Reproducibility Report for "Learning To Count Objects In Natural Images For Visual Question Answering"0
Did the Model Understand the Question?Code0
Reciprocal Attention Fusion for Visual Question Answering0
Edit me: A Corpus and a Framework for Understanding Natural Language Image Editing0
Augmenting Image Question Answering Dataset by Exploiting Image Captions0
Action Verb Corpus0
Visual Choice of Plausible Alternatives: An Evaluation of Image-based Commonsense Causal ReasoningCode0
Enhancing the AI2 Diagrams Dataset Using Rhetorical Structure TheoryCode0
Large Scale Scene Text Verification with Guided Attention0
ECG Heartbeat Classification: A Deep Transferable RepresentationCode0
Question Type Guided Attention in Visual Question Answering0
Improved Fusion of Visual and Language Representations by Dense Symmetric Co-Attention for Visual Question AnsweringCode0
Differential Attention for Visual Question AnsweringCode0
Visual Question Reasoning on General Dependency Tree0
DDRprog: A CLEVR Differentiable Dynamic Reasoning Programmer0
Two can play this Game: Visual Dialog with Discriminative Question Generation and Answering0
Motion-Appearance Co-Memory Networks for Video Question Answering0
Generalized Hadamard-Product Fusion Operators for Visual Question Answering0
Explicit Reasoning over End-to-End Neural Architectures for Visual Question Answering0
Attention on Attention: Architectures for Visual Question Answering (VQA)Code0
VQA-E: Explaining, Elaborating, and Enhancing Your Answers for Visual Questions0
Inverse Visual Question Answering: A New Benchmark and VQA Diagnosis Tool0
A Dataset and Architecture for Visual Reasoning with a Working MemoryCode0
Transparency by Design: Closing the Gap Between Performance and Interpretability in Visual ReasoningCode0
Compositional Attention Networks for Machine ReasoningCode1
VizWiz Grand Challenge: Answering Visual Questions from Blind PeopleCode0
Learning to Count Objects in Natural Images for Visual 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
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