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 201250 of 2167 papers

TitleStatusHype
Generic Attention-model Explainability for Interpreting Bi-Modal and Encoder-Decoder TransformersCode1
GeneAnnotator: A Semi-automatic Annotation Tool for Visual Scene GraphCode1
Generalizing from SIMPLE to HARD Visual Reasoning: Can We Mitigate Modality Imbalance in VLMs?Code1
Going Full-TILT Boogie on Document Understanding with Text-Image-Layout TransformerCode1
Genixer: Empowering Multimodal Large Language Models as a Powerful Data GeneratorCode1
From the Least to the Most: Building a Plug-and-Play Visual Reasoner via Data SynthesisCode1
FunQA: Towards Surprising Video ComprehensionCode1
Graph Optimal Transport for Cross-Domain AlignmentCode1
2BiVQA: Double Bi-LSTM based Video Quality Assessment of UGC VideosCode1
GRIT: General Robust Image Task BenchmarkCode1
HallE-Control: Controlling Object Hallucination in Large Multimodal ModelsCode1
Attention in Reasoning: Dataset, Analysis, and ModelingCode1
Awaker2.5-VL: Stably Scaling MLLMs with Parameter-Efficient Mixture of ExpertsCode1
HIDRO-VQA: High Dynamic Range Oracle for Video Quality AssessmentCode1
How Many Unicorns Are in This Image? A Safety Evaluation Benchmark for Vision LLMsCode1
How Much Can CLIP Benefit Vision-and-Language Tasks?Code1
BadCM: Invisible Backdoor Attack Against Cross-Modal LearningCode1
Gemini Goes to Med School: Exploring the Capabilities of Multimodal Large Language Models on Medical Challenge Problems & HallucinationsCode1
I Can't Believe There's No Images! Learning Visual Tasks Using only Language SupervisionCode1
IconQA: A New Benchmark for Abstract Diagram Understanding and Visual Language ReasoningCode1
GeoLLaVA-8K: Scaling Remote-Sensing Multimodal Large Language Models to 8K ResolutionCode1
GraghVQA: Language-Guided Graph Neural Networks for Graph-based Visual Question AnsweringCode1
Improving Selective Visual Question Answering by Learning from Your PeersCode1
A Comparison of Pre-trained Vision-and-Language Models for Multimodal Representation Learning across Medical Images and ReportsCode1
An Empirical Analysis on Spatial Reasoning Capabilities of Large Multimodal ModelsCode1
Learning to Answer Visual Questions from Web VideosCode1
3D-Aware Visual Question Answering about Parts, Poses and OcclusionsCode1
Attention-Based Context Aware Reasoning for Situation RecognitionCode1
FloodNet: A High Resolution Aerial Imagery Dataset for Post Flood Scene UnderstandingCode1
Inter-GPS: Interpretable Geometry Problem Solving with Formal Language and Symbolic ReasoningCode1
A Comprehensive Evaluation of GPT-4V on Knowledge-Intensive Visual Question AnsweringCode1
Change Detection Meets Visual Question AnsweringCode1
An Empirical Study of End-to-End Video-Language Transformers with Masked Visual ModelingCode1
Fine-grained Image Classification and Retrieval by Combining Visual and Locally Pooled Textual FeaturesCode1
An Empirical Study of GPT-3 for Few-Shot Knowledge-Based VQACode1
Just Ask: Learning to Answer Questions from Millions of Narrated VideosCode1
Beyond Question-Based Biases: Assessing Multimodal Shortcut Learning in Visual Question AnsweringCode1
An Empirical Study of Multimodal Model MergingCode1
Beyond Task Performance: Evaluating and Reducing the Flaws of Large Multimodal Models with In-Context LearningCode1
An Empirical Study of Training End-to-End Vision-and-Language TransformersCode1
Can I Trust Your Answer? Visually Grounded Video Question AnsweringCode1
Bilateral Cross-Modality Graph Matching Attention for Feature Fusion in Visual Question AnsweringCode1
Florence: A New Foundation Model for Computer VisionCode1
FAVER: Blind Quality Prediction of Variable Frame Rate VideosCode1
Language-Informed Visual Concept LearningCode1
Language Prior Is Not the Only Shortcut: A Benchmark for Shortcut Learning in VQACode1
Check It Again:Progressive Visual Question Answering via Visual EntailmentCode1
Blindly Assess Quality of In-the-Wild Videos via Quality-aware Pre-training and Motion PerceptionCode1
Check It Again: Progressive Visual Question Answering via Visual EntailmentCode1
A Symmetric Dual Encoding Dense Retrieval Framework for Knowledge-Intensive 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