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

Showing 16261650 of 2167 papers

TitleStatusHype
X-LXMERT: Paint, Caption and Answer Questions with Multi-Modal TransformersCode1
Regularizing Attention Networks for Anomaly Detection in Visual Question Answering0
MUTANT: A Training Paradigm for Out-of-Distribution Generalization in Visual Question AnsweringCode1
A Multimodal Memes Classification: A Survey and Open Research Issues0
A Comparison of Pre-trained Vision-and-Language Models for Multimodal Representation Learning across Medical Images and ReportsCode1
Cross-modal Knowledge Reasoning for Knowledge-based Visual Question Answering0
A Dataset and Baselines for Visual Question Answering on ArtCode1
Visual Question Answering on Image Sets0
No-Reference Video Quality Assessment Using Space-Time ChipsCode0
Document Visual Question Answering Challenge 20200
Linguistically-aware Attention for Reducing the Semantic-Gap in Vision-Language Tasks0
DeVLBert: Learning Deconfounded Visio-Linguistic RepresentationsCode1
Graph Edit Distance Reward: Learning to Edit Scene Graph0
Assisting Scene Graph Generation with Self-Supervision0
TRRNet: Tiered Relation Reasoning for Compositional Visual Question Answering0
Interpretable Visual Reasoning via Probabilistic Formulation under Natural Supervision0
Noise-Induced Barren Plateaus in Variational Quantum AlgorithmsCode0
REXUP: I REason, I EXtract, I UPdate with Structured Compositional Reasoning for Visual Question AnsweringCode0
Contrastive Visual-Linguistic PretrainingCode0
Dialog without Dialog Data: Learning Visual Dialog Agents from VQA DataCode0
Spatially Aware Multimodal Transformers for TextVQACode1
Semantic Equivalent Adversarial Data Augmentation for Visual Question AnsweringCode1
Knowledge-Based Video Question Answering with Unsupervised Scene DescriptionsCode1
Learning to Discretely Compose Reasoning Module Networks for Video CaptioningCode1
Reducing Language Biases in Visual Question Answering with Visually-Grounded Question Encoder0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1humanAccuracy89.3Unverified
2DREAM+Unicoder-VL (MSRA)Accuracy76.04Unverified
3TRRNet (Ensemble)Accuracy74.03Unverified
4MIL-nbgaoAccuracy73.81Unverified
5Kakao BrainAccuracy73.33Unverified
6Coarse-to-Fine Reasoning, Single ModelAccuracy72.14Unverified
7270Accuracy70.23Unverified
8NSM ensemble (updated)Accuracy67.55Unverified
9VinVL-DPTAccuracy64.92Unverified
10VinVL+LAccuracy64.85Unverified
#ModelMetricClaimedVerifiedStatus
1PaLIAccuracy84.3Unverified
2BEiT-3Accuracy84.19Unverified
3VLMoAccuracy82.78Unverified
4ONE-PEACEAccuracy82.6Unverified
5mPLUG (Huge)Accuracy82.43Unverified
6CuMo-7BAccuracy82.2Unverified
7X2-VLM (large)Accuracy81.9Unverified
8MMUAccuracy81.26Unverified
9LyricsAccuracy81.2Unverified
10InternVL-CAccuracy81.2Unverified
#ModelMetricClaimedVerifiedStatus
1BEiT-3overall84.03Unverified
2mPLUG-Hugeoverall83.62Unverified
3ONE-PEACEoverall82.52Unverified
4X2-VLM (large)overall81.8Unverified
5VLMooverall81.3Unverified
6SimVLMoverall80.34Unverified
7X2-VLM (base)overall80.2Unverified
8VASToverall80.19Unverified
9VALORoverall78.62Unverified
10Prompt Tuningoverall78.53Unverified