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 18011850 of 2167 papers

TitleStatusHype
Ludwig: a type-based declarative deep learning toolboxCode3
Inverse Visual Question Answering with Multi-Level Attentions0
Sunny and Dark Outside?! Improving Answer Consistency in VQA through Entailed Question Generation0
Don't Take the Easy Way Out: Ensemble Based Methods for Avoiding Known Dataset BiasesCode1
Decoupled Box Proposal and Featurization with Ultrafine-Grained Semantic Labels Improve Image Captioning and Visual Question Answering0
PlotQA: Reasoning over Scientific Plots0
Adversarial Representation Learning for Text-to-Image Matching0
Visual Question Answering using Deep Learning: A Survey and Performance AnalysisCode0
VL-BERT: Pre-training of Generic Visual-Linguistic RepresentationsCode1
LXMERT: Learning Cross-Modality Encoder Representations from TransformersCode1
Language Features Matter: Effective Language Representations for Vision-Language Tasks0
What is needed for simple spatial language capabilities in VQA?0
U-CAM: Visual Explanation using Uncertainty based Class Activation Maps0
VideoNavQA: Bridging the Gap between Visual and Embodied Question AnsweringCode1
Fusion of Detected Objects in Text for Visual Question Answering0
Reactive Multi-Stage Feature Fusion for Multimodal Dialogue Modeling0
Why Does a Visual Question Have Different Answers?0
Multimodal Unified Attention Networks for Vision-and-Language Interactions0
Multi-modality Latent Interaction Network for Visual Question Answering0
Question-Agnostic Attention for Visual Question Answering0
VisualBERT: A Simple and Performant Baseline for Vision and LanguageCode1
CRIC: A VQA Dataset for Compositional Reasoning on Vision and Commonsense0
ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language TasksCode1
Answering Questions about Data Visualizations using Efficient Bimodal FusionCode0
The Meaning of ``Most'' for Visual Question Answering Models0
An Empirical Study of Batch Normalization and Group Normalization in Conditional Computation0
LEAF-QA: Locate, Encode & Attend for Figure Question Answering0
An Empirical Study on Leveraging Scene Graphs for Visual Question Answering0
Bilinear Graph Networks for Visual Question Answering0
KVQA: Knowledge-Aware Visual Question Answering0
OmniNet: A unified architecture for multi-modal multi-task learningCode0
2nd Place Solution to the GQA Challenge 20190
Assessing Visual Quality of Omnidirectional Videos0
Neural Reasoning, Fast and Slow, for Video Question Answering0
Learning by Abstraction: The Neural State MachineCode0
Are Red Roses Red? Evaluating Consistency of Question-Answering ModelsCode0
Multi-grained Attention with Object-level Grounding for Visual Question Answering0
ICDAR 2019 Competition on Scene Text Visual Question Answering0
Deep Modular Co-Attention Networks for Visual Question AnsweringCode0
RUBi: Reducing Unimodal Biases in Visual Question AnsweringCode0
Integrating Knowledge and Reasoning in Image Understanding0
Adversarial Multimodal Network for Movie Question Answering0
Investigating Biases in Textual Entailment Datasets0
Two-Level Approach for No-Reference Consumer Video Quality AssessmentCode0
Adversarial Regularization for Visual Question Answering: Strengths, Shortcomings, and Side Effects0
Improving Visual Question Answering by Referring to Generated Paragraph Captions0
Mimic and Fool: A Task Agnostic Adversarial AttackCode0
Psycholinguistics meets Continual Learning: Measuring Catastrophic Forgetting in Visual Question Answering0
ActivityNet-QA: A Dataset for Understanding Complex Web Videos via Question AnsweringCode0
Generating Question Relevant Captions to Aid Visual Question Answering0
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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