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 551600 of 2167 papers

TitleStatusHype
NLX-GPT: A Model for Natural Language Explanations in Vision and Vision-Language TasksCode1
Generative Bias for Robust Visual Question AnsweringCode1
Are Bias Mitigation Techniques for Deep Learning Effective?Code1
GeoLLaVA-8K: Scaling Remote-Sensing Multimodal Large Language Models to 8K ResolutionCode1
End-to-end Document Recognition and Understanding with DessurtCode1
Genixer: Empowering Multimodal Large Language Models as a Powerful Data GeneratorCode1
End-to-end Knowledge Retrieval with Multi-modal QueriesCode1
Calibrating Concepts and Operations: Towards Symbolic Reasoning on Real ImagesCode1
Notes-guided MLLM Reasoning: Enhancing MLLM with Knowledge and Visual Notes for Visual Question AnsweringCode1
Bottom-Up and Top-Down Attention for Image Captioning and Visual Question AnsweringCode1
DualVGR: A Dual-Visual Graph Reasoning Unit for Video Question AnsweringCode1
Going Full-TILT Boogie on Document Understanding with Text-Image-Layout TransformerCode1
Can I Trust Your Answer? Visually Grounded Video Question AnsweringCode1
FaceBench: A Multi-View Multi-Level Facial Attribute VQA Dataset for Benchmarking Face Perception MLLMsCode1
Natural Language Rationales with Full-Stack Visual Reasoning: From Pixels to Semantic Frames to Commonsense GraphsCode1
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based LocalizationCode1
NExT-QA:Next Phase of Question-Answering to Explaining Temporal ActionsCode1
Graph Optimal Transport for Cross-Domain AlignmentCode1
Can Pre-trained Vision and Language Models Answer Visual Information-Seeking Questions?Code1
GRIT: General Robust Image Task BenchmarkCode1
Enhancing Vision-Language Pre-Training with Jointly Learned Questioner and Dense CaptionerCode1
Enhancing Visual Question Answering through Question-Driven Image Captions as PromptsCode1
Boosting the Power of Small Multimodal Reasoning Models to Match Larger Models with Self-Consistency TrainingCode1
Dual-Key Multimodal Backdoors for Visual Question AnsweringCode1
eP-ALM: Efficient Perceptual Augmentation of Language ModelsCode1
OK-VQA: A Visual Question Answering Benchmark Requiring External KnowledgeCode1
Exploring Opinion-unaware Video Quality Assessment with Semantic Affinity CriterionCode1
ERNIE-Layout: Layout Knowledge Enhanced Pre-training for Visually-rich Document UnderstandingCode1
NExT-QA: Next Phase of Question-Answering to Explaining Temporal ActionsCode1
Hierarchical multimodal transformers for Multi-Page DocVQACode1
GPT-4V-AD: Exploring Grounding Potential of VQA-oriented GPT-4V for Zero-shot Anomaly DetectionCode1
Hierarchical Conditional Relation Networks for Video Question AnsweringCode1
FFAA: Multimodal Large Language Model based Explainable Open-World Face Forgery Analysis AssistantCode1
Introspective Distillation for Robust Question AnsweringCode1
How Many Unicorns Are in This Image? A Safety Evaluation Benchmark for Vision LLMsCode1
Evaluating Image Hallucination in Text-to-Image Generation with Question-AnsweringCode1
MUTANT: A Training Paradigm for Out-of-Distribution Generalization in Visual Question AnsweringCode1
Overcoming Language Priors with Self-supervised Learning for Visual Question AnsweringCode1
How to Configure Good In-Context Sequence for Visual Question AnsweringCode1
Towards More Faithful Natural Language Explanation Using Multi-Level Contrastive Learning in VQACode1
DrishtiKon: Multi-Granular Visual Grounding for Text-Rich Document ImagesCode0
BLOCK: Bilinear Superdiagonal Fusion for Visual Question Answering and Visual Relationship DetectionCode0
Don't Just Assume; Look and Answer: Overcoming Priors for Visual Question AnsweringCode0
Multimodal Residual Learning for Visual QACode0
Blind VQA on 360° Video via Progressively Learning from Pixels, Frames and VideoCode0
Blind Prediction of Natural Video QualityCode0
A Neuro-Symbolic ASP Pipeline for Visual Question AnsweringCode0
Multimodal Explanations: Justifying Decisions and Pointing to the EvidenceCode0
Document Haystacks: Vision-Language Reasoning Over Piles of 1000+ DocumentsCode0
Biomedical Visual Instruction Tuning with Clinician Preference AlignmentCode0
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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