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 1451–1475 of 2167 papers

TitleStatusHype
NAAQA: A Neural Architecture for Acoustic Question AnsweringCode0
Supervising the Transfer of Reasoning Patterns in VQA—0
Bayesian Attention Belief Networks—0
PAM: Understanding Product Images in Cross Product Category Attribute Extraction—0
Check It Again: Progressive Visual Question Answering via Visual EntailmentCode1
Are VQA Systems RAD? Measuring Robustness to Augmented Data with Focused Interventions—0
Human-Adversarial Visual Question Answering—0
Grounding Complex Navigational Instructions Using Scene Graphs—0
Semantic Aligned Multi-modal Transformer for Vision-LanguageUnderstanding: A Preliminary Study on Visual QA—0
Learning to Select Question-Relevant Relations for Visual Question Answering—0
MiniVQA - A resource to build your tailored VQA competition—0
CLEVR\_HYP: A Challenge Dataset and Baselines for Visual Question Answering with Hypothetical Actions over ImagesCode0
MIMOQA: Multimodal Input Multimodal Output Question Answering—0
EaSe: A Diagnostic Tool for VQA based on Answer DiversityCode0
Adversarial VQA: A New Benchmark for Evaluating the Robustness of VQA Models—0
LPF: A Language-Prior Feedback Objective Function for De-biased Visual Question AnsweringCode0
StructuralLM: Structural Pre-training for Form Understanding—0
Multi-modal Understanding and Generation for Medical Images and Text via Vision-Language Pre-TrainingCode1
Probing Inter-modality: Visual Parsing with Self-Attention for Vision-and-Language Pre-training—0
Multiple Meta-model Quantifying for Medical Visual Question AnsweringCode1
NExT-QA:Next Phase of Question-Answering to Explaining Temporal ActionsCode1
Survey of Visual-Semantic Embedding Methods for Zero-Shot Image Retrieval—0
Show Why the Answer is Correct! Towards Explainable AI using Compositional Temporal Attention—0
Cross-Modal Generative Augmentation for Visual Question Answering—0
Found a Reason for me? Weakly-supervised Grounded Visual Question Answering using CapsulesCode1
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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