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 13511400 of 2167 papers

TitleStatusHype
Correlation Information Bottleneck: Towards Adapting Pretrained Multimodal Models for Robust Visual Question Answering0
PreSTU: Pre-Training for Scene-Text Understanding0
Pre-training image-language transformers for open-vocabulary tasks0
Improving the Cross-Lingual Generalisation in Visual Question AnsweringCode0
Evaluating Point Cloud from Moving Camera Videos: A No-Reference MetricCode0
Bidirectional Contrastive Split Learning for Visual Question Answering0
FashionVQA: A Domain-Specific Visual Question Answering System0
How good are deep models in understanding the generated images?0
Image as a Foreign Language: BEiT Pretraining for All Vision and Vision-Language TasksCode0
VLMAE: Vision-Language Masked Autoencoder0
Understanding Attention for Vision-and-Language TasksCode0
ILLUME: Rationalizing Vision-Language Models through Human InteractionsCode0
Aesthetic Visual Question Answering of Photographs0
Prompt Tuning for Generative Multimodal Pretrained Models0
NAPA: Intermediate-level Variational Native-pulse Ansatz for Variational Quantum Algorithms0
Video Question Answering with Iterative Video-Text Co-Tokenization0
Parameter-Parallel Distributed Variational Quantum Algorithm0
Uncertainty-based Visual Question Answering: Estimating Semantic Inconsistency between Image and Knowledge Base0
Towards Complex Document Understanding By Discrete Reasoning0
Is GPT-3 all you need for Visual Question Answering in Cultural Heritage?0
WinoGAViL: Gamified Association Benchmark to Challenge Vision-and-Language ModelsCode0
Visual Perturbation-aware Collaborative Learning for Overcoming the Language Prior Problem0
Semantic-aware Modular Capsule Routing for Visual Question Answering0
QSAN: A Near-term Achievable Quantum Self-Attention Network0
Multiview Contrastive Learning for Completely Blind Video Quality Assessment of User Generated ContentCode0
Subjective and Objective Quality Assessment of High-Motion Sports Videos at Low-BitratesCode0
Exploring the Effectiveness of Video Perceptual Representation in Blind Video Quality AssessmentCode0
OVQA: A Clinically Generated Visual Question Answering Dataset0
Knowing Earlier what Right Means to You: A Comprehensive VQA Dataset for Grounding Relative Directions via Multi-Task LearningCode0
VGNMN: Video-grounded Neural Module Networks for Video-Grounded Dialogue Systems0
American == White in Multimodal Language-and-Image AI0
Modern Question Answering Datasets and Benchmarks: A Survey0
EBMs vs. CL: Exploring Self-Supervised Visual Pretraining for Visual Question Answering0
From Shallow to Deep: Compositional Reasoning over Graphs for Visual Question Answering0
VisFIS: Visual Feature Importance Supervision with Right-for-the-Right-Reason ObjectivesCode0
Tell Me the Evidence? Dual Visual-Linguistic Interaction for Answer Grounding0
DisCoVQA: Temporal Distortion-Content Transformers for Video Quality AssessmentCode0
Grounding Answers for Visual Questions Asked by Visually Impaired People0
Unified-IO: A Unified Model for Vision, Language, and Multi-Modal Tasks0
Test-Time Adaptation for Visual Document Understanding0
Language Models are General-Purpose Interfaces0
Less Is More: Linear Layers on CLIP Features as Powerful VizWiz Model0
cViL: Cross-Lingual Training of Vision-Language Models using Knowledge DistillationCode0
From Pixels to Objects: Cubic Visual Attention for Visual Question Answering0
Structured Two-stream Attention Network for Video Question Answering0
VL-BEiT: Generative Vision-Language Pretraining0
Un jeu de données pour répondre à des questions visuelles à propos d’entités nommées en utilisant des bases de connaissances (ViQuAE, a Dataset for Knowledge-based Visual Question Answering about Named Entities)0
Question Modifiers in Visual Question Answering0
Fine-tuning vs From Scratch: Do Vision & Language Models Have Similar Capabilities on Out-of-Distribution Visual Question Answering?0
An Efficient Modern Baseline for FloodNet VQACode0
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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