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 301350 of 2167 papers

TitleStatusHype
Greedy Gradient Ensemble for Robust Visual Question AnsweringCode1
Change Detection Meets Visual Question AnsweringCode1
Are Vision Language Models Ready for Clinical Diagnosis? A 3D Medical Benchmark for Tumor-centric Visual Question AnsweringCode1
GRIT: General Robust Image Task BenchmarkCode1
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based LocalizationCode1
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
Going Full-TILT Boogie on Document Understanding with Text-Image-Layout TransformerCode1
A Good Prompt Is Worth Millions of Parameters: Low-resource Prompt-based Learning for Vision-Language ModelsCode1
Graph Optimal Transport for Cross-Domain AlignmentCode1
Graphhopper: Multi-Hop Scene Graph Reasoning for Visual Question AnsweringCode1
HallE-Control: Controlling Object Hallucination in Large Multimodal ModelsCode1
Harnessing the Power of Multi-Task Pretraining for Ground-Truth Level Natural Language ExplanationsCode1
Check It Again: Progressive Visual Question Answering via Visual EntailmentCode1
Check It Again:Progressive Visual Question Answering via Visual EntailmentCode1
ChipQA: No-Reference Video Quality Prediction via Space-Time ChipsCode1
ChiQA: A Large Scale Image-based Real-World Question Answering Dataset for Multi-Modal UnderstandingCode1
Hierarchical multimodal transformers for Multi-Page DocVQACode1
How to Configure Good In-Context Sequence for Visual Question AnsweringCode1
UniTAB: Unifying Text and Box Outputs for Grounded Vision-Language ModelingCode1
Classification-Regression for Chart ComprehensionCode1
I Can't Believe There's No Images! Learning Visual Tasks Using only Language SupervisionCode1
Genixer: Empowering Multimodal Large Language Models as a Powerful Data GeneratorCode1
Comprehensive Visual Question Answering on Point Clouds through Compositional Scene ManipulationCode1
CLEVR: A Diagnostic Dataset for Compositional Language and Elementary Visual ReasoningCode1
Conceptual 12M: Pushing Web-Scale Image-Text Pre-Training To Recognize Long-Tail Visual ConceptsCode1
Generic Attention-model Explainability for Interpreting Bi-Modal and Encoder-Decoder TransformersCode1
GeoLLaVA-8K: Scaling Remote-Sensing Multimodal Large Language Models to 8K ResolutionCode1
ActiView: Evaluating Active Perception Ability for Multimodal Large Language ModelsCode1
ConTEXTual Net: A Multimodal Vision-Language Model for Segmentation of PneumothoraxCode1
CLEVR-X: A Visual Reasoning Dataset for Natural Language ExplanationsCode1
AIGV-Assessor: Benchmarking and Evaluating the Perceptual Quality of Text-to-Video Generation with LMMCode1
InfMLLM: A Unified Framework for Visual-Language TasksCode1
Generalizing from SIMPLE to HARD Visual Reasoning: Can We Mitigate Modality Imbalance in VLMs?Code1
AIM 2024 Challenge on Compressed Video Quality Assessment: Methods and ResultsCode1
Counterfactual Samples Synthesizing and Training for Robust Visual Question AnsweringCode1
Introspective Distillation for Robust Question AnsweringCode1
Investigating Prompting Techniques for Zero- and Few-Shot Visual Question AnsweringCode1
Closed Loop Neural-Symbolic Learning via Integrating Neural Perception, Grammar Parsing, and Symbolic ReasoningCode1
Clover: Towards A Unified Video-Language Alignment and Fusion ModelCode1
Coarse-to-Fine Reasoning for Visual Question AnsweringCode1
Coarse-to-Fine Vision-Language Pre-training with Fusion in the BackboneCode1
Counterfactual Samples Synthesizing for Robust Visual Question AnsweringCode1
Kosmos-2: Grounding Multimodal Large Language Models to the WorldCode1
CAT-ViL: Co-Attention Gated Vision-Language Embedding for Visual Question Localized-Answering in Robotic SurgeryCode1
GeneAnnotator: A Semi-automatic Annotation Tool for Visual Scene GraphCode1
COBRA: Contrastive Bi-Modal Representation AlgorithmCode1
CoCa: Contrastive Captioners are Image-Text Foundation ModelsCode1
Counterfactual VQA: A Cause-Effect Look at Language BiasCode1
Generative Bias for Robust Visual 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