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 12511300 of 2167 papers

TitleStatusHype
Enabling Multimodal Generation on CLIP via Vision-Language Knowledge Distillation0
NLX-GPT: A Model for Natural Language Explanations in Vision and Vision-Language TasksCode1
AssistQ: Affordance-centric Question-driven Task Completion for Egocentric AssistantCode1
Barlow constrained optimization for Visual Question AnsweringCode0
Dynamic Key-value Memory Enhanced Multi-step Graph Reasoning for Knowledge-based Visual Question AnsweringCode0
Modeling Coreference Relations in Visual Dialog0
Recent, rapid advancement in visual question answering architecture: a review0
Unsupervised Vision-and-Language Pre-training via Retrieval-based Multi-Granular Alignment0
Joint Answering and Explanation for Visual Commonsense ReasoningCode0
On Modality Bias Recognition and ReductionCode0
Measuring CLEVRness: Blackbox testing of Visual Reasoning Models0
Vision-Language Pre-Training with Triple Contrastive LearningCode2
OG-SGG: Ontology-Guided Scene Graph Generation. A Case Study in Transfer Learning for Telepresence RoboticsCode0
RankDVQA: Deep VQA based on Ranking-inspired Hybrid Training0
Privacy Preserving Visual Question Answering0
Delving Deeper into Cross-lingual Visual Question AnsweringCode0
An experimental study of the vision-bottleneck in VQA0
Can Open Domain Question Answering Systems Answer Visual Knowledge Questions?0
NEWSKVQA: Knowledge-Aware News Video Question Answering0
OFA: Unifying Architectures, Tasks, and Modalities Through a Simple Sequence-to-Sequence Learning FrameworkCode0
Webly Supervised Concept Expansion for General Purpose Vision Models0
Grounding Answers for Visual Questions Asked by Visually Impaired PeopleCode0
Compositionality as Lexical SymmetryCode0
BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and GenerationCode5
IGLUE: A Benchmark for Transfer Learning across Modalities, Tasks, and LanguagesCode1
Transformer Module Networks for Systematic Generalization in Visual Question AnsweringCode0
Learning to Compose Diversified Prompts for Image Emotion Classification0
MGA-VQA: Multi-Granularity Alignment for Visual Question Answering0
SA-VQA: Structured Alignment of Visual and Semantic Representations for Visual Question Answering0
Question Generation for Evaluating Cross-Dataset Shifts in Multi-modal Grounding0
KAT: A Knowledge Augmented Transformer for Vision-and-Language0
Retrieving Visual Facts For Few-Shot Visual Question Answering0
MANGO: Enhancing the Robustness of VQA Models via Adversarial Noise Generation0
All You May Need for VQA are Image Captions0
Task Formulation Matters When Learning Continuously: A Case Study in Visual Question Answering0
Probing the Role of Positional Information in Vision-Language Models0
CLIP-TD: CLIP Targeted Distillation for Vision-Language Tasks0
A Thousand Words Are Worth More Than a Picture: Natural Language-Centric Outside-Knowledge Visual Question Answering0
Towards Automated Error Analysis: Learning to Characterize Errors0
On the Efficacy of Co-Attention Transformer Layers in Visual Question Answering0
Uni-EDEN: Universal Encoder-Decoder Network by Multi-Granular Vision-Language Pre-training0
COIN: Counterfactual Image Generation for VQA Interpretation0
FAVER: Blind Quality Prediction of Variable Frame Rate VideosCode1
Interactive Attention AI to translate low light photos to captions for night scene understanding in women safety0
V-Doc: Visual Questions Answers With Documents0
Transform-Retrieve-Generate: Natural Language-Centric Outside-Knowledge Visual Question Answering0
Query and Attention Augmentation for Knowledge-Based Explainable ReasoningCode0
Towards General Purpose Vision Systems: An End-to-End Task-Agnostic Vision-Language Architecture0
Maintaining Reasoning Consistency in Compositional Visual Question AnsweringCode1
Multi-Image 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