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 1701–1725 of 2167 papers

TitleStatusHype
LOIS: Looking Out of Instance Semantics for Visual Question Answering—0
Long-Form Answers to Visual Questions from Blind and Low Vision People—0
Look, Learn and Leverage (L^3): Mitigating Visual-Domain Shift and Discovering Intrinsic Relations via Symbolic Alignment—0
Look, Read and Ask: Learning to Ask Questions by Reading Text in Images—0
Losing Visual Needles in Image Haystacks: Vision Language Models are Easily Distracted in Short and Long Contexts—0
Zero-shot and Few-shot Learning with Knowledge Graphs: A Comprehensive Survey—0
LRRA:A Transparent Neural-Symbolic Reasoning Framework for Real-World Visual Question Answering—0
Can You Explain That? Lucid Explanations Help Human-AI Collaborative Image Retrieval—0
Lyrics: Boosting Fine-grained Language-Vision Alignment and Comprehension via Semantic-aware Visual Objects—0
M4CXR: Exploring Multi-task Potentials of Multi-modal Large Language Models for Chest X-ray Interpretation—0
MAGIC-VQA: Multimodal And Grounded Inference with Commonsense Knowledge for Visual Question Answering—0
Making the V in Text-VQA Matter—0
Making Video Quality Assessment Models Sensitive to Frame Rate Distortions—0
Making Video Quality Assessment Models Robust to Bit Depth—0
MAMO: Masked Multimodal Modeling for Fine-Grained Vision-Language Representation Learning—0
MANGO: Enhancing the Robustness of VQA Models via Adversarial Noise Generation—0
Mask4Align: Aligned Entity Prompting with Color Masks for Multi-Entity Localization Problems—0
MatCha: Enhancing Visual Language Pretraining with Math Reasoning and Chart Derendering—0
Measuring CLEVRness: Black-box Testing of Visual Reasoning Models—0
Measuring CLEVRness: Blackbox testing of Visual Reasoning Models—0
Measuring Machine Intelligence Through Visual Question Answering—0
Med-2E3: A 2D-Enhanced 3D Medical Multimodal Large Language Model—0
MedFrameQA: A Multi-Image Medical VQA Benchmark for Clinical Reasoning—0
Medical Visual Question Answering: A Survey—0
Medical visual question answering using joint self-supervised learning—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