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 1251–1275 of 2167 papers

TitleStatusHype
FVQA 2.0: Introducing Adversarial Samples into Fact-based Visual Question Answering—0
Logical Implications for Visual Question Answering ConsistencyCode0
Breaking Common Sense: WHOOPS! A Vision-and-Language Benchmark of Synthetic and Compositional Images—0
MRET: Multi-resolution Transformer for Video Quality Assessment—0
Polar-VQA: Visual Question Answering on Remote Sensed Ice sheet Imagery from Polar Region—0
Vision-Language Models as Success Detectors—0
MuLTI: Efficient Video-and-Language Understanding with Text-Guided MultiWay-Sampler and Multiple Choice Modeling—0
Understanding and Constructing Latent Modality Structures in Multi-modal Representation Learning—0
Toward Unsupervised Realistic Visual Question Answering—0
Interpretable Visual Question Answering Referring to Outside Knowledge—0
Graph Neural Networks in Vision-Language Image Understanding: A Survey—0
Knowledge-Based Counterfactual Queries for Visual Question Answering—0
VTQA: Visual Text Question Answering via Entity Alignment and Cross-Media ReasoningCode0
Audio-Visual Quality Assessment for User Generated Content: Database and Method—0
VQA with Cascade of Self- and Co-Attention Blocks—0
Medical visual question answering using joint self-supervised learning—0
EVJVQA Challenge: Multilingual Visual Question Answering—0
VinVL+L: Enriching Visual Representation with Location Context in VQACode0
Few-shot Multimodal Multitask Multilingual Learning—0
Interpretable Medical Image Visual Question Answering via Multi-Modal Relationship Graph Learning—0
Bridge Damage Cause Estimation Using Multiple Images Based on Visual Question Answering—0
Differentiable Outlier Detection Enable Robust Deep Multimodal AnalysisCode0
Is Multimodal Vision Supervision Beneficial to Language?Code0
BinaryVQA: A Versatile Test Set to Evaluate the Out-of-Distribution Generalization of VQA ModelsCode0
Towards a Unified Model for Generating Answers and Explanations in 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