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 501550 of 2167 papers

TitleStatusHype
Just Ask: Learning to Answer Questions from Millions of Narrated VideosCode1
LIVE: Learnable In-Context Vector for Visual Question AnsweringCode1
Improving Selective Visual Question Answering by Learning from Your PeersCode1
IllusionVQA: A Challenging Optical Illusion Dataset for Vision Language ModelsCode1
In Defense of Grid Features for Visual Question AnsweringCode1
I Can't Believe There's No Images! Learning Visual Tasks Using only Language SupervisionCode1
How to Configure Good In-Context Sequence for Visual Question AnsweringCode1
How Much Can CLIP Benefit Vision-and-Language Tasks?Code1
IconQA: A New Benchmark for Abstract Diagram Understanding and Visual Language ReasoningCode1
Hierarchical multimodal transformers for Multi-Page DocVQACode1
HIDRO-VQA: High Dynamic Range Oracle for Video Quality AssessmentCode1
Hierarchical Question-Image Co-Attention for Visual Question AnsweringCode1
HAAR: Text-Conditioned Generative Model of 3D Strand-based Human HairstylesCode1
Dynamic Language Binding in Relational Visual ReasoningCode1
Harnessing the Power of Multi-Task Pretraining for Ground-Truth Level Natural Language ExplanationsCode1
How Many Unicorns Are in This Image? A Safety Evaluation Benchmark for Vision LLMsCode1
InfMLLM: A Unified Framework for Visual-Language TasksCode1
GQA: A New Dataset for Real-World Visual Reasoning and Compositional Question AnsweringCode1
GraghVQA: Language-Guided Graph Neural Networks for Graph-based Visual Question AnsweringCode1
End-to-end Knowledge Retrieval with Multi-modal QueriesCode1
Graphhopper: Multi-Hop Scene Graph Reasoning for Visual Question AnsweringCode1
Are Bias Mitigation Techniques for Deep Learning Effective?Code1
Bottom-Up and Top-Down Attention for Image Captioning and Visual Question AnsweringCode1
DualVGR: A Dual-Visual Graph Reasoning Unit for Video Question AnsweringCode1
End-to-end Document Recognition and Understanding with DessurtCode1
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based LocalizationCode1
Going Full-TILT Boogie on Document Understanding with Text-Image-Layout TransformerCode1
GRIT: General Robust Image Task BenchmarkCode1
Graph Optimal Transport for Cross-Domain AlignmentCode1
HallE-Control: Controlling Object Hallucination in Large Multimodal ModelsCode1
EarthVQA: Towards Queryable Earth via Relational Reasoning-Based Remote Sensing Visual Question AnsweringCode1
Break It Down: A Question Understanding BenchmarkCode1
Hierarchical Conditional Relation Networks for Video Question AnsweringCode1
Genixer: Empowering Multimodal Large Language Models as a Powerful Data GeneratorCode1
Generic Attention-model Explainability for Interpreting Bi-Modal and Encoder-Decoder TransformersCode1
GeoLLaVA-8K: Scaling Remote-Sensing Multimodal Large Language Models to 8K ResolutionCode1
HYDRA: A Hyper Agent for Dynamic Compositional Visual ReasoningCode1
Hypergraph Transformer: Weakly-supervised Multi-hop Reasoning for Knowledge-based Visual Question AnsweringCode1
Bridging the Gap between 2D and 3D Visual Question Answering: A Fusion Approach for 3D VQACode1
IGLUE: A Benchmark for Transfer Learning across Modalities, Tasks, and LanguagesCode1
IMPACT: A Large-scale Integrated Multimodal Patent Analysis and Creation Dataset for Design PatentsCode1
Boosting the Power of Small Multimodal Reasoning Models to Match Larger Models with Self-Consistency TrainingCode1
Dual-Key Multimodal Backdoors for Visual Question AnsweringCode1
EHRXQA: A Multi-Modal Question Answering Dataset for Electronic Health Records with Chest X-ray ImagesCode1
Enhancing Visual Question Answering through Question-Driven Image Captions as PromptsCode1
eP-ALM: Efficient Perceptual Augmentation of Language ModelsCode1
FaceBench: A Multi-View Multi-Level Facial Attribute VQA Dataset for Benchmarking Face Perception MLLMsCode1
InternVL: Scaling up Vision Foundation Models and Aligning for Generic Visual-Linguistic TasksCode1
Introspective Distillation for Robust Question AnsweringCode1
Evaluating Image Hallucination in Text-to-Image Generation with Question-AnsweringCode1
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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