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 21012150 of 2167 papers

TitleStatusHype
A Dataset for Multimodal Question Answering in the Cultural Heritage Domain0
Modeling Relationships in Referential Expressions with Compositional Modular NetworksCode0
Grad-CAM: Why did you say that?Code0
Zero-Shot Visual Question Answering0
Leveraging Video Descriptions to Learn Video Question Answering0
Dual Attention Networks for Multimodal Reasoning and MatchingCode0
Proposing Plausible Answers for Open-ended Visual Question Answering0
Hadamard Product for Low-rank Bilinear PoolingCode0
Open-Ended Visual Question-AnsweringCode0
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based LocalizationCode1
Visual Question Answering: Datasets, Algorithms, and Future ChallengesCode0
Tutorial on Answering Questions about Images with Deep LearningCode0
The Color of the Cat is Gray: 1 Million Full-Sentences Visual Question Answering (FSVQA)0
Graph-Structured Representations for Visual Question Answering0
Measuring Machine Intelligence Through Visual Question Answering0
Towards Transparent AI Systems: Interpreting Visual Question Answering Models0
Visual Question: Predicting If a Crowd Will Agree on the Answer0
Solving Visual Madlibs with Multiple Cues0
``Look, some Green Circles!'': Learning to Quantify from Images0
A Shared Task on Multimodal Machine Translation and Crosslingual Image Description0
Focused Evaluation for Image Description with Binary Forced-Choice Tasks0
Visual Question Answering: A Survey of Methods and DatasetsCode0
Annotation Methodologies for Vision and Language Dataset Creation0
Revisiting Visual Question Answering BaselinesCode0
Analyzing the Behavior of Visual Question Answering ModelsCode0
Question Relevance in VQA: Identifying Non-Visual And False-Premise Questions0
DualNet: Domain-Invariant Network for Visual Question Answering0
Human Attention in Visual Question Answering: Do Humans and Deep Networks Look at the Same Regions?0
FVQA: Fact-based Visual Question Answering0
Training Recurrent Answering Units with Joint Loss Minimization for VQA0
Human Attention in Visual Question Answering: Do Humans and Deep Networks Look at the Same Regions?0
Multimodal Compact Bilinear Pooling for Visual Question Answering and Visual GroundingCode0
Multimodal Residual Learning for Visual QACode0
Answer-Type Prediction for Visual Question Answering0
``A Distorted Skull Lies in the Bottom Center...'' Identifying Paintings from Text Descriptions0
Hierarchical Question-Image Co-Attention for Visual Question AnsweringCode1
End-to-End Instance Segmentation with Recurrent AttentionCode0
Ask Your Neurons: A Deep Learning Approach to Visual Question AnsweringCode0
Leveraging Visual Question Answering for Image-Caption Ranking0
Learning Models for Actions and Person-Object Interactions with Transfer to Question Answering0
Counting Everyday Objects in Everyday ScenesCode0
A Focused Dynamic Attention Model for Visual Question Answering0
Neural Attention Models for Sequence Classification: Analysis and Application to Key Term Extraction and Dialogue Act Detection0
A Diagram Is Worth A Dozen ImagesCode0
Image Captioning and Visual Question Answering Based on Attributes and External Knowledge0
Dynamic Memory Networks for Visual and Textual Question AnsweringCode0
Neural Self Talk: Image Understanding via Continuous Questioning and Answering0
Simple Baseline for Visual Question AnsweringCode0
A Restricted Visual Turing Test for Deep Scene and Event Understanding0
Are You Talking to a Machine? Dataset and Methods for Multilingual Image Question0
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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