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 251300 of 2167 papers

TitleStatusHype
Investigating Prompting Techniques for Zero- and Few-Shot Visual Question AnsweringCode1
Introspective Distillation for Robust Question AnsweringCode1
JDocQA: Japanese Document Question Answering Dataset for Generative Language ModelsCode1
Fast Prompt Alignment for Text-to-Image GenerationCode1
AssistQ: Affordance-centric Question-driven Task Completion for Egocentric AssistantCode1
Interpreting Chest X-rays Like a Radiologist: A Benchmark with Clinical ReasoningCode1
Just Ask: Learning to Answer Questions from Millions of Narrated VideosCode1
DeVLBert: Learning Deconfounded Visio-Linguistic RepresentationsCode1
Align before Fuse: Vision and Language Representation Learning with Momentum DistillationCode1
Detecting Hate Speech in Multi-modal MemesCode1
DocVQA: A Dataset for VQA on Document ImagesCode1
InternVL: Scaling up Vision Foundation Models and Aligning for Generic Visual-Linguistic TasksCode1
KAT: A Knowledge Augmented Transformer for Vision-and-LanguageCode1
Language-Informed Visual Concept LearningCode1
Align and Prompt: Video-and-Language Pre-training with Entity PromptsCode1
InfMLLM: A Unified Framework for Visual-Language TasksCode1
Describe Anything Model for Visual Question Answering on Text-rich ImagesCode1
Deep Multimodal Neural Architecture SearchCode1
In Defense of Grid Features for Visual Question AnsweringCode1
IMPACT: A Large-scale Integrated Multimodal Patent Analysis and Creation Dataset for Design PatentsCode1
IllusionVQA: A Challenging Optical Illusion Dataset for Vision Language ModelsCode1
IconQA: A New Benchmark for Abstract Diagram Understanding and Visual Language ReasoningCode1
AIM 2024 Challenge on Compressed Video Quality Assessment: Methods and ResultsCode1
IGLUE: A Benchmark for Transfer Learning across Modalities, Tasks, and LanguagesCode1
Improving Selective Visual Question Answering by Learning from Your PeersCode1
Instruction-Guided Visual MaskingCode1
Declaration-based Prompt Tuning for Visual Question AnsweringCode1
AIGV-Assessor: Benchmarking and Evaluating the Perceptual Quality of Text-to-Video Generation with LMMCode1
ActiView: Evaluating Active Perception Ability for Multimodal Large Language ModelsCode1
AI2-THOR: An Interactive 3D Environment for Visual AICode1
Debiasing Multimodal Models via Causal Information MinimizationCode1
Hypergraph Transformer: Weakly-supervised Multi-hop Reasoning for Knowledge-based Visual Question AnsweringCode1
Does Vision-and-Language Pretraining Improve Lexical Grounding?Code1
Detecting and Preventing Hallucinations in Large Vision Language ModelsCode1
Debiased Visual Question Answering from Feature and Sample PerspectivesCode1
DataEnvGym: Data Generation Agents in Teacher Environments with Student FeedbackCode1
Decoupled Seg Tokens Make Stronger Reasoning Video Segmenter and GrounderCode1
HYDRA: A Hyper Agent for Dynamic Compositional Visual ReasoningCode1
I Can't Believe There's No Images! Learning Visual Tasks Using only Language SupervisionCode1
Inter-GPS: Interpretable Geometry Problem Solving with Formal Language and Symbolic ReasoningCode1
Language Prior Is Not the Only Shortcut: A Benchmark for Shortcut Learning in VQACode1
Cross-modal Retrieval for Knowledge-based Visual Question AnsweringCode1
A Hitchhikers Guide to Fine-Grained Face Forgery Detection Using Common Sense ReasoningCode1
Hierarchical multimodal transformers for Multi-Page DocVQACode1
Cross-Modality Relevance for Reasoning on Language and VisionCode1
Hierarchical Conditional Relation Networks for Video Question AnsweringCode1
Hierarchical Question-Image Co-Attention for Visual Question AnsweringCode1
A Good Prompt Is Worth Millions of Parameters: Low-resource Prompt-based Learning for Vision-Language ModelsCode1
Are Vision Language Models Ready for Clinical Diagnosis? A 3D Medical Benchmark for Tumor-centric Visual Question AnsweringCode1
Harnessing the Power of Multi-Task Pretraining for Ground-Truth Level Natural Language ExplanationsCode1
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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