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 10511100 of 2167 papers

TitleStatusHype
A survey on knowledge-enhanced multimodal learning0
CL-CrossVQA: A Continual Learning Benchmark for Cross-Domain Visual Question Answering0
Visual Programming: Compositional visual reasoning without trainingCode2
Text-Aware Dual Routing Network for Visual Question Answering0
I Can't Believe There's No Images! Learning Visual Tasks Using only Language SupervisionCode1
AlignVE: Visual Entailment Recognition Based on Alignment Relations0
PromptCap: Prompt-Guided Task-Aware Image CaptioningCode1
MapQA: A Dataset for Question Answering on Choropleth MapsCode1
Visually Grounded VQA by Lattice-based RetrievalCode0
Learning to Answer Multilingual and Code-Mixed Questions0
Why Did the Chicken Cross the Road? Rephrasing and Analyzing Ambiguous Questions in VQACode0
MF2-MVQA: A Multi-stage Feature Fusion method for Medical Visual Question Answering0
Watching the News: Towards VideoQA Models that can Read0
Towards Reasoning-Aware Explainable VQA0
ERNIE-UniX2: A Unified Cross-lingual Cross-modal Framework for Understanding and Generation0
Exploring Video Quality Assessment on User Generated Contents from Aesthetic and Technical PerspectivesCode2
Visual Named Entity Linking: A New Dataset and A BaselineCode1
CRIPP-VQA: Counterfactual Reasoning about Implicit Physical Properties via Video Question AnsweringCode0
Generalization Differences between End-to-End and Neuro-Symbolic Vision-Language Reasoning Systems0
What's Different between Visual Question Answering for Machine "Understanding" Versus for Accessibility?Code0
Compressing And Debiasing Vision-Language Pre-Trained Models for Visual Question AnsweringCode0
VLC-BERT: Visual Question Answering with Contextualized Commonsense KnowledgeCode1
Learning by Hallucinating: Vision-Language Pre-training with Weak Supervision0
RSVG: Exploring Data and Models for Visual Grounding on Remote Sensing Data0
PoseScript: Linking 3D Human Poses and Natural LanguageCode2
Image Semantic Relation Generation0
Aligning MAGMA by Few-Shot Learning and Finetuning0
Entity-Focused Dense Passage Retrieval for Outside-Knowledge Visual Question Answering0
Plug-and-Play VQA: Zero-shot VQA by Conjoining Large Pretrained Models with Zero TrainingCode0
Meta-Learning via Classifier(-free) Diffusion GuidanceCode1
Vision-Language Pre-training: Basics, Recent Advances, and Future TrendsCode3
MAPL: Parameter-Efficient Adaptation of Unimodal Pre-Trained Models for Vision-Language Few-Shot PromptingCode1
ERNIE-Layout: Layout Knowledge Enhanced Pre-training for Visually-rich Document UnderstandingCode1
SlotFormer: Unsupervised Visual Dynamics Simulation with Object-Centric ModelsCode1
Neighbourhood Representative Sampling for Efficient End-to-end Video Quality AssessmentCode2
MAP: Multimodal Uncertainty-Aware Vision-Language Pre-training ModelCode1
Language Prior Is Not the Only Shortcut: A Benchmark for Shortcut Learning in VQACode1
DCVQE: A Hierarchical Transformer for Video Quality Assessment0
Towards Robust Visual Question Answering: Making the Most of Biased Samples via Contrastive LearningCode1
Multi-Modal Fusion Transformer for Visual Question Answering in Remote Sensing0
MAMO: Masked Multimodal Modeling for Fine-Grained Vision-Language Representation Learning0
HVS Revisited: A Comprehensive Video Quality Assessment Framework0
Retrieval Augmented Visual Question Answering with Outside KnowledgeCode2
Pix2Struct: Screenshot Parsing as Pretraining for Visual Language UnderstandingCode2
Learning to Collocate Visual-Linguistic Neural Modules for Image CaptioningCode0
Enhancing Interpretability and Interactivity in Robot Manipulation: A Neurosymbolic ApproachCode0
On the Effects of Video Grounding on Language Models0
Dual Capsule Attention Mask Network with Mutual Learning for Visual Question Answering0
A Dual-Attention Learning Network with Word and Sentence Embedding for Medical Visual Question AnsweringCode0
Task Formulation Matters When Learning Continually: A Case Study in 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