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 151200 of 2167 papers

TitleStatusHype
Bottom-Up and Top-Down Attention for Image Captioning and Visual Question AnsweringCode1
Harnessing the Power of Multi-Task Pretraining for Ground-Truth Level Natural Language ExplanationsCode1
Graphhopper: Multi-Hop Scene Graph Reasoning for Visual Question AnsweringCode1
Graph Optimal Transport for Cross-Domain AlignmentCode1
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based LocalizationCode1
An Empirical Study of Training End-to-End Vision-and-Language TransformersCode1
GraghVQA: Language-Guided Graph Neural Networks for Graph-based Visual Question AnsweringCode1
Greedy Gradient Ensemble for Robust Visual Question AnsweringCode1
HIDRO-VQA: High Dynamic Range Oracle for Video Quality AssessmentCode1
An Empirical Study of Multimodal Model MergingCode1
An Empirical Study of GPT-3 for Few-Shot Knowledge-Based VQACode1
An Empirical Study of End-to-End Video-Language Transformers with Masked Visual ModelingCode1
A Comprehensive Evaluation of GPT-4V on Knowledge-Intensive Visual Question AnsweringCode1
Going Full-TILT Boogie on Document Understanding with Text-Image-Layout TransformerCode1
3D-Aware Visual Question Answering about Parts, Poses and OcclusionsCode1
An Empirical Analysis on Spatial Reasoning Capabilities of Large Multimodal ModelsCode1
Visual Grounding Methods for VQA are Working for the Wrong Reasons!Code1
A Comparison of Pre-trained Vision-and-Language Models for Multimodal Representation Learning across Medical Images and ReportsCode1
GQA: A New Dataset for Real-World Visual Reasoning and Compositional Question AnsweringCode1
Hierarchical Conditional Relation Networks for Video Question AnsweringCode1
Generic Attention-model Explainability for Interpreting Bi-Modal and Encoder-Decoder TransformersCode1
Blindly Assess Quality of In-the-Wild Videos via Quality-aware Pre-training and Motion PerceptionCode1
Genixer: Empowering Multimodal Large Language Models as a Powerful Data GeneratorCode1
GeneAnnotator: A Semi-automatic Annotation Tool for Visual Scene GraphCode1
Gemini Goes to Med School: Exploring the Capabilities of Multimodal Large Language Models on Medical Challenge Problems & HallucinationsCode1
Generalizing from SIMPLE to HARD Visual Reasoning: Can We Mitigate Modality Imbalance in VLMs?Code1
Analysis of Video Quality Datasets via Design of Minimalistic Video Quality ModelsCode1
FunQA: Towards Surprising Video ComprehensionCode1
Generative Bias for Robust Visual Question AnsweringCode1
GeoLLaVA-8K: Scaling Remote-Sensing Multimodal Large Language Models to 8K ResolutionCode1
FoodieQA: A Multimodal Dataset for Fine-Grained Understanding of Chinese Food CultureCode1
AMD-Hummingbird: Towards an Efficient Text-to-Video ModelCode1
Found a Reason for me? Weakly-supervised Grounded Visual Question Answering using CapsulesCode1
A Dataset and Baselines for Visual Question Answering on ArtCode1
FlowLearn: Evaluating Large Vision-Language Models on Flowchart UnderstandingCode1
Fine-grained Image Classification and Retrieval by Combining Visual and Locally Pooled Textual FeaturesCode1
FiLM: Visual Reasoning with a General Conditioning LayerCode1
FloodNet: A High Resolution Aerial Imagery Dataset for Post Flood Scene UnderstandingCode1
Fast Prompt Alignment for Text-to-Image GenerationCode1
Boosting the Power of Small Multimodal Reasoning Models to Match Larger Models with Self-Consistency TrainingCode1
FAVER: Blind Quality Prediction of Variable Frame Rate VideosCode1
2BiVQA: Double Bi-LSTM based Video Quality Assessment of UGC VideosCode1
Attention in Reasoning: Dataset, Analysis, and ModelingCode1
FFAA: Multimodal Large Language Model based Explainable Open-World Face Forgery Analysis AssistantCode1
Florence: A New Foundation Model for Computer VisionCode1
Bilateral Cross-Modality Graph Matching Attention for Feature Fusion in Visual Question AnsweringCode1
From the Least to the Most: Building a Plug-and-Play Visual Reasoner via Data SynthesisCode1
Hierarchical multimodal transformers for Multi-Page DocVQACode1
Expert Knowledge-Aware Image Difference Graph Representation Learning for Difference-Aware Medical Visual Question AnsweringCode1
Attention-Based Context Aware Reasoning for Situation RecognitionCode1
Show:102550
← PrevPage 4 of 44Next →

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