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 1551–1600 of 2167 papers

TitleStatusHype
Weakly Supervised Relative Spatial Reasoning for Visual Question AnsweringCode0
A review of Quantum Neural Networks: Methods, Models, Dilemma—0
QACE: Asking Questions to Evaluate an Image CaptionCode0
On the Significance of Question Encoder Sequence Model in the Out-of-Distribution Performance in Visual Question Answering—0
Auto-Parsing Network for Image Captioning and Visual Question Answering—0
EKTVQA: Generalized use of External Knowledge to empower Scene Text in Text-VQA—0
StarVQA: Space-Time Attention for Video Quality AssessmentCode0
Localize, Group, and Select: Boosting Text-VQA by Scene Text Modeling—0
VALSE: A Task-Independent Benchmark for Vision and Language Models centered on Linguistic Phenomena—0
BERTHop: An Effective Vision-and-Language Model for Chest X-ray Disease DiagnosisCode0
Towards Visual Question Answering on Pathology ImagesCode0
LRRA:A Transparent Neural-Symbolic Reasoning Framework for Real-World Visual Question Answering—0
利用图像描述与知识图谱增强表示的视觉问答(Exploiting Image Captions and External Knowledge as Representation Enhancement for Visual Question Answering)—0
In Factuality: Efficient Integration of Relevant Facts for Visual Question Answering—0
X-GGM: Graph Generative Modeling for Out-of-Distribution Generalization in Visual Question AnsweringCode0
MuVAM: A Multi-View Attention-based Model for Medical Visual Question Answering—0
Cognitive Visual Commonsense Reasoning Using Dynamic Working MemoryCode0
Adventurer's Treasure Hunt: A Transparent System for Visually Grounded Compositional Visual Question Answering based on Scene Graphs—0
Multimodal Few-Shot Learning with Frozen Language Models—0
A Picture May Be Worth a Hundred Words for Visual Question Answering—0
Probing Inter-modality: Visual Parsing with Self-Attention for Vision-Language Pre-training—0
A Transformer-based Cross-modal Fusion Model with Adversarial Training for VQA Challenge 2021—0
FOVQA: Blind Foveated Video Quality Assessment—0
VQA-Aid: Visual Question Answering for Post-Disaster Damage Assessment and Analysis—0
How Modular Should Neural Module Networks Be for Systematic Generalization?Code0
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