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 14511500 of 2167 papers

TitleStatusHype
OG-SGG: Ontology-Guided Scene Graph Generation. A Case Study in Transfer Learning for Telepresence RoboticsCode0
RankDVQA: Deep VQA based on Ranking-inspired Hybrid Training0
Delving Deeper into Cross-lingual Visual Question AnsweringCode0
Privacy Preserving Visual Question Answering0
An experimental study of the vision-bottleneck in VQA0
Can Open Domain Question Answering Systems Answer Visual Knowledge Questions?0
NEWSKVQA: Knowledge-Aware News Video Question Answering0
OFA: Unifying Architectures, Tasks, and Modalities Through a Simple Sequence-to-Sequence Learning FrameworkCode0
Grounding Answers for Visual Questions Asked by Visually Impaired PeopleCode0
Webly Supervised Concept Expansion for General Purpose Vision Models0
Compositionality as Lexical SymmetryCode0
Transformer Module Networks for Systematic Generalization in Visual Question AnsweringCode0
Learning to Compose Diversified Prompts for Image Emotion Classification0
MGA-VQA: Multi-Granularity Alignment for Visual Question Answering0
SA-VQA: Structured Alignment of Visual and Semantic Representations for Visual Question Answering0
Question Generation for Evaluating Cross-Dataset Shifts in Multi-modal Grounding0
KAT: A Knowledge Augmented Transformer for Vision-and-Language0
All You May Need for VQA are Image Captions0
Task Formulation Matters When Learning Continuously: A Case Study in Visual Question Answering0
Probing the Role of Positional Information in Vision-Language Models0
Retrieving Visual Facts For Few-Shot Visual Question Answering0
MANGO: Enhancing the Robustness of VQA Models via Adversarial Noise Generation0
CLIP-TD: CLIP Targeted Distillation for Vision-Language Tasks0
A Thousand Words Are Worth More Than a Picture: Natural Language-Centric Outside-Knowledge Visual Question Answering0
Towards Automated Error Analysis: Learning to Characterize Errors0
On the Efficacy of Co-Attention Transformer Layers in Visual Question Answering0
Uni-EDEN: Universal Encoder-Decoder Network by Multi-Granular Vision-Language Pre-training0
COIN: Counterfactual Image Generation for VQA Interpretation0
Interactive Attention AI to translate low light photos to captions for night scene understanding in women safety0
Transform-Retrieve-Generate: Natural Language-Centric Outside-Knowledge Visual Question Answering0
Query and Attention Augmentation for Knowledge-Based Explainable ReasoningCode0
Towards General Purpose Vision Systems: An End-to-End Task-Agnostic Vision-Language Architecture0
V-Doc: Visual Questions Answers With Documents0
Does CLIP Benefit Visual Question Answering in the Medical Domain as Much as it Does in the General Domain?0
Multi-Image Visual Question AnsweringCode0
General Greedy De-bias LearningCode0
Task-Oriented Multi-User Semantic Communications0
Zero-shot and Few-shot Learning with Knowledge Graphs: A Comprehensive Survey0
Understanding Attention for Vision-and-Language Tasks0
3D Question Answering0
Improving and Diagnosing Knowledge-Based Visual Question Answering via Entity Enhanced Knowledge Injection0
Unified Multimodal Pre-training and Prompt-based Tuning for Vision-Language Understanding and Generation0
MoCA: Incorporating Multi-stage Domain Pretraining and Cross-guided Multimodal Attention for Textbook Question Answering0
Curriculum Learning Effectively Improves Low Data VQA0
Robust Visual Reasoning via Language Guided Neural Module Networks0
eaVQA: An Experimental Analysis on Visual Question Answering Models0
Scallop: From Probabilistic Deductive Databases to Scalable Differentiable Reasoning0
LiVLR: A Lightweight Visual-Linguistic Reasoning Framework for Video Question Answering0
Scene Graph Generation with Geometric Context0
A Confidence-Based Interface for Neuro-Symbolic Visual Question Answering0
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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