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 14011450 of 2167 papers

TitleStatusHype
TxT: Crossmodal End-to-End Learning with Transformers0
Weakly-Supervised Visual-Retriever-Reader for Knowledge-based Question AnsweringCode1
GeneAnnotator: A Semi-automatic Annotation Tool for Visual Scene GraphCode1
Improved RAMEN: Towards Domain Generalization for Visual Question AnsweringCode0
Weakly Supervised Relative Spatial Reasoning for Visual Question AnsweringCode0
A review of Quantum Neural Networks: Methods, Models, Dilemma0
WebQA: Multihop and Multimodal QACode1
QACE: Asking Questions to Evaluate an Image CaptionCode0
On the Significance of Question Encoder Sequence Model in the Out-of-Distribution Performance in Visual Question Answering0
SimVLM: Simple Visual Language Model Pretraining with Weak SupervisionCode1
Auto-Parsing Network for Image Captioning and Visual Question Answering0
EKTVQA: Generalized use of External Knowledge to empower Scene Text in Text-VQA0
StarVQA: Space-Time Attention for Video Quality AssessmentCode0
Localize, Group, and Select: Boosting Text-VQA by Scene Text Modeling0
Blindly Assess Quality of In-the-Wild Videos via Quality-aware Pre-training and Motion PerceptionCode1
X-modaler: A Versatile and High-performance Codebase for Cross-modal AnalyticsCode1
VALSE: A Task-Independent Benchmark for Vision and Language Models centered on Linguistic Phenomena0
Task-Oriented Multi-User Semantic Communications for VQA TaskCode1
BERTHop: An Effective Vision-and-Language Model for Chest X-ray Disease DiagnosisCode0
Sparse Continuous Distributions and Fenchel-Young LossesCode1
LRRA:A Transparent Neural-Symbolic Reasoning Framework for Real-World Visual Question Answering0
利用图像描述与知识图谱增强表示的视觉问答(Exploiting Image Captions and External Knowledge as Representation Enhancement for Visual Question Answering)0
Check It Again:Progressive Visual Question Answering via Visual EntailmentCode1
Towards Visual Question Answering on Pathology ImagesCode0
In Factuality: Efficient Integration of Relevant Facts for Visual Question Answering0
Greedy Gradient Ensemble for Robust Visual Question AnsweringCode1
X-GGM: Graph Generative Modeling for Out-of-Distribution Generalization in Visual Question AnsweringCode0
Separating Skills and Concepts for Novel Visual Question AnsweringCode1
Align before Fuse: Vision and Language Representation Learning with Momentum DistillationCode1
How Much Can CLIP Benefit Vision-and-Language Tasks?Code1
Graphhopper: Multi-Hop Scene Graph Reasoning for Visual Question AnsweringCode1
Zero-shot Visual Question Answering using Knowledge GraphCode1
DualVGR: A Dual-Visual Graph Reasoning Unit for Video Question AnsweringCode1
MuVAM: A Multi-View Attention-based Model for Medical Visual Question Answering0
Mind Your Outliers! Investigating the Negative Impact of Outliers on Active Learning for Visual Question AnsweringCode1
Cognitive Visual Commonsense Reasoning Using Dynamic Working MemoryCode0
Adventurer's Treasure Hunt: A Transparent System for Visually Grounded Compositional Visual Question Answering based on Scene Graphs0
Multimodal Few-Shot Learning with Frozen Language Models0
Probing Inter-modality: Visual Parsing with Self-Attention for Vision-Language Pre-training0
A Picture May Be Worth a Hundred Words for Visual Question Answering0
FOVQA: Blind Foveated Video Quality Assessment0
A Transformer-based Cross-modal Fusion Model with Adversarial Training for VQA Challenge 20210
NExT-QA: Next Phase of Question-Answering to Explaining Temporal ActionsCode1
Perception Matters: Detecting Perception Failures of VQA Models Using Metamorphic TestingCode1
Predicting Human Scanpaths in Visual Question AnsweringCode1
RSTNet: Captioning With Adaptive Attention on Visual and Non-Visual WordsCode1
VQA-Aid: Visual Question Answering for Post-Disaster Damage Assessment and Analysis0
Probing Image-Language Transformers for Verb UnderstandingCode1
Assessment of Subjective and Objective Quality of Live Streaming Sports Videos0
How Modular Should Neural Module Networks Be for Systematic Generalization?Code0
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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