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 951–975 of 2167 papers

TitleStatusHype
Generalization Differences between End-to-End and Neuro-Symbolic Vision-Language Reasoning Systems—0
Assessing Image Quality Issues for Real-World Problems—0
Aligned Dual Channel Graph Convolutional Network for Visual Question Answering—0
Language Models are General-Purpose Interfaces—0
Can You Explain That? Lucid Explanations Help Human-AI Collaborative Image Retrieval—0
MatCha: Enhancing Visual Language Pretraining with Math Reasoning and Chart Derendering—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
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
Look, Learn and Leverage (L^3): Mitigating Visual-Domain Shift and Discovering Intrinsic Relations via Symbolic Alignment—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
Look, Read and Ask: Learning to Ask Questions by Reading Text in Images—0
Asking questions on handwritten document collections—0
LAVIS: A Library for Language-Vision Intelligence—0
CoBIT: A Contrastive Bi-directional Image-Text Generation Model—0
LOIS: Looking Out of Instance Semantics for Visual Question Answering—0
FVQA: Fact-based Visual Question Answering—0
FVQA 2.0: Introducing Adversarial Samples into Fact-based Visual Question Answering—0
Fusion of Domain-Adapted Vision and Language Models for Medical Visual Question Answering—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
9LyricsAccuracy81.2—Unverified
10InternVL-CAccuracy81.2—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