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 1576–1600 of 2167 papers

TitleStatusHype
Assessment of Subjective and Objective Quality of Live Streaming Sports Videos—0
NAAQA: A Neural Architecture for Acoustic Question AnsweringCode0
Supervising the Transfer of Reasoning Patterns in VQA—0
Bayesian Attention Belief Networks—0
Are VQA Systems RAD? Measuring Robustness to Augmented Data with Focused Interventions—0
PAM: Understanding Product Images in Cross Product Category Attribute Extraction—0
Human-Adversarial Visual Question Answering—0
Grounding Complex Navigational Instructions Using Scene Graphs—0
CLEVR\_HYP: A Challenge Dataset and Baselines for Visual Question Answering with Hypothetical Actions over ImagesCode0
Adversarial VQA: A New Benchmark for Evaluating the Robustness of VQA Models—0
Learning to Select Question-Relevant Relations for Visual Question Answering—0
MiniVQA - A resource to build your tailored VQA competition—0
Semantic Aligned Multi-modal Transformer for Vision-LanguageUnderstanding: A Preliminary Study on Visual QA—0
MIMOQA: Multimodal Input Multimodal Output Question Answering—0
EaSe: A Diagnostic Tool for VQA based on Answer DiversityCode0
LPF: A Language-Prior Feedback Objective Function for De-biased Visual Question AnsweringCode0
StructuralLM: Structural Pre-training for Form Understanding—0
Probing Inter-modality: Visual Parsing with Self-Attention for Vision-and-Language Pre-training—0
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
AdaVQA: Overcoming Language Priors with Adapted Margin Cosine LossCode0
Proposal-free One-stage Referring Expression via Grid-Word Cross-Attention—0
Iterated learning for emergent systematicity in VQA—0
A survey on VQA_Datasets and Approaches—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