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

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Showing 951–1000 of 2167 papers

TitleStatusHype
Generating Triples with Adversarial Networks for Scene Graph Construction—0
Generating Rationales in Visual Question Answering—0
Assessing Visual Quality of Omnidirectional Videos—0
Language Models are General-Purpose Interfaces—0
Ontology-based knowledge representation for bone disease diagnosis: a foundation for safe and sustainable medical artificial intelligence systems—0
Generating Natural Questions from Images for Multimodal Assistants—0
LAPDoc: Layout-Aware Prompting for Documents—0
Generating Natural Language Explanations for Visual Question Answering using Scene Graphs and Visual Attention—0
COIN: Counterfactual Image Generation for VQA Interpretation—0
DiffVQA: Video Quality Assessment Using Diffusion Feature Extractor—0
Generate then Select: Open-ended Visual Question Answering Guided by World Knowledge—0
Assessing the Robustness of Visual Question Answering Models—0
Generalized Hadamard-Product Fusion Operators for Visual Question Answering—0
Generalization Differences between End-to-End and Neuro-Symbolic Vision-Language Reasoning Systems—0
Directional Gradient Projection for Robust Fine-Tuning of Foundation Models—0
Latent Image and Video Resolution Prediction using Convolutional Neural Networks—0
Latent Variable Models for Visual Question Answering—0
Assessing Image Quality Issues for Real-World Problems—0
Aligned Dual Channel Graph Convolutional Network for Visual Question Answering—0
LAVIS: A Library for Language-Vision Intelligence—0
Gender and Racial Bias in Visual Question Answering Datasets—0
Gemini Pro Defeated by GPT-4V: Evidence from Education—0
COCO is "ALL'' You Need for Visual Instruction Fine-tuning—0
Ask Me Anything: Free-form Visual Question Answering Based on Knowledge from External Sources—0
GEMeX-ThinkVG: Towards Thinking with Visual Grounding in Medical VQA via Reinforcement Learning—0
LEAF-QA: Locate, Encode & Attend for Figure Question Answering—0
GEMeX: A Large-Scale, Groundable, and Explainable Medical VQA Benchmark for Chest X-ray Diagnosis—0
GC-KBVQA: A New Four-Stage Framework for Enhancing Knowledge Based Visual Question Answering Performance—0
Making Video Quality Assessment Models Robust to Bit Depth—0
Learning by Asking Questions—0
Learning by Hallucinating: Vision-Language Pre-training with Weak Supervision—0
Learning Compositional Representation for Few-shot Visual Question Answering—0
Asking questions on handwritten document collections—0
CoBIT: A Contrastive Bi-directional Image-Text Generation Model—0
Binding Touch to Everything: Learning Unified Multimodal Tactile Representations—0
FVQA: Fact-based Visual Question Answering—0
FVQA 2.0: Introducing Adversarial Samples into Fact-based Visual Question Answering—0
Learning Models for Actions and Person-Object Interactions with Transfer to Question Answering—0
Learning Reasoning Paths over Semantic Graphs for Video-grounded Dialogues—0
Document AI: Benchmarks, Models and Applications—0
Making the V in Text-VQA Matter—0
Fusion of Domain-Adapted Vision and Language Models for Medical Visual Question Answering—0
Learning Sparse Mixture of Experts for Visual Question Answering—0
Learning to Answer Multilingual and Code-Mixed Questions—0
Fusion of Detected Objects in Text for Visual Question Answering—0
Ada-DQA: Adaptive Diverse Quality-aware Feature Acquisition for Video Quality Assessment—0
FunBench: Benchmarking Fundus Reading Skills of MLLMs—0
Asking More Informative Questions for Grounded Retrieval—0
MAGIC-VQA: Multimodal And Grounded Inference with Commonsense Knowledge for Visual Question Answering—0
Making Video Quality Assessment Models Sensitive to Frame Rate Distortions—0
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All datasetsGQA Test2019VQA v2 test-devVQA v2 test-stdOK-VQAMSVD-QADocVQA testMSRVTT-QAInfographicVQAGQA test-devVizWiz 2020 VQAA-OKVQACLEVR

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1humanAccuracy89.3—Unverified
2DREAM+Unicoder-VL (MSRA)Accuracy76.04—Unverified
3TRRNet (Ensemble)Accuracy74.03—Unverified
4MIL-nbgaoAccuracy73.81—Unverified
5Kakao BrainAccuracy73.33—Unverified
6Coarse-to-Fine Reasoning, Single ModelAccuracy72.14—Unverified
7270Accuracy70.23—Unverified
8NSM ensemble (updated)Accuracy67.55—Unverified
9VinVL-DPTAccuracy64.92—Unverified
10VinVL+LAccuracy64.85—Unverified
#ModelMetricClaimedVerifiedStatus
1PaLIAccuracy84.3—Unverified
2BEiT-3Accuracy84.19—Unverified
3VLMoAccuracy82.78—Unverified
4ONE-PEACEAccuracy82.6—Unverified
5mPLUG (Huge)Accuracy82.43—Unverified
6CuMo-7BAccuracy82.2—Unverified
7X2-VLM (large)Accuracy81.9—Unverified
8MMUAccuracy81.26—Unverified
9InternVL-CAccuracy81.2—Unverified
10LyricsAccuracy81.2—Unverified
#ModelMetricClaimedVerifiedStatus
1BEiT-3overall84.03—Unverified
2mPLUG-Hugeoverall83.62—Unverified
3ONE-PEACEoverall82.52—Unverified
4X2-VLM (large)overall81.8—Unverified
5VLMooverall81.3—Unverified
6SimVLMoverall80.34—Unverified
7X2-VLM (base)overall80.2—Unverified
8VASToverall80.19—Unverified
9VALORoverall78.62—Unverified
10Prompt Tuningoverall78.53—Unverified