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 1301–1325 of 2167 papers

TitleStatusHype
Visual Question Answering From Another Perspective: CLEVR Mental Rotation TestsCode0
Compound Tokens: Channel Fusion for Vision-Language Representation Learning—0
Semi-supervised Learning of Perceptual Video Quality by Generating Consistent Pairwise Pseudo-Ranks—0
Optimizing Explanations by Network Canonization and Hyperparameter Search—0
PiggyBack: Pretrained Visual Question Answering Environment for Backing up Non-deep Learning Professionals—0
Neuro-Symbolic Spatio-Temporal Reasoning—0
Look, Read and Ask: Learning to Ask Questions by Reading Text in Images—0
A Short Survey of Systematic Generalization—0
Cross-Modal Contrastive Learning for Robust Reasoning in VQACode0
Enhancing Self-Consistency and Performance of Pre-Trained Language Models through Natural Language Inference—0
A survey on knowledge-enhanced multimodal learning—0
CL-CrossVQA: A Continual Learning Benchmark for Cross-Domain Visual Question Answering—0
Text-Aware Dual Routing Network for Visual Question Answering—0
AlignVE: Visual Entailment Recognition Based on Alignment Relations—0
Visually Grounded VQA by Lattice-based RetrievalCode0
Why Did the Chicken Cross the Road? Rephrasing and Analyzing Ambiguous Questions in VQACode0
Learning to Answer Multilingual and Code-Mixed Questions—0
MF2-MVQA: A Multi-stage Feature Fusion method for Medical Visual Question Answering—0
Watching the News: Towards VideoQA Models that can Read—0
Towards Reasoning-Aware Explainable VQA—0
ERNIE-UniX2: A Unified Cross-lingual Cross-modal Framework for Understanding and Generation—0
CRIPP-VQA: Counterfactual Reasoning about Implicit Physical Properties via Video Question AnsweringCode0
Compressing And Debiasing Vision-Language Pre-Trained Models for Visual Question AnsweringCode0
What's Different between Visual Question Answering for Machine "Understanding" Versus for Accessibility?Code0
Generalization Differences between End-to-End and Neuro-Symbolic Vision-Language Reasoning Systems—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