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

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 1276–1300 of 2167 papers

Title	Date	Tasks	Status	Hype
Transformer Module Networks for Systematic Generalization in Visual Question Answering	Jan 27, 2022	Question AnsweringSystematic Generalization	CodeCode Available	0
Learning to Compose Diversified Prompts for Image Emotion Classification	Jan 26, 2022	ClassificationEmotion Classification	—Unverified	0
MGA-VQA: Multi-Granularity Alignment for Visual Question Answering	Jan 25, 2022	Question AnsweringVisual Question Answering	—Unverified	0
SA-VQA: Structured Alignment of Visual and Semantic Representations for Visual Question Answering	Jan 25, 2022	Question AnsweringVisual Question Answering	—Unverified	0
Question Generation for Evaluating Cross-Dataset Shifts in Multi-modal Grounding	Jan 24, 2022	Question AnsweringQuestion Generation	—Unverified	0
KAT: A Knowledge Augmented Transformer for Vision-and-Language	Jan 16, 2022	Answer GenerationDecoder	—Unverified	0
Retrieving Visual Facts For Few-Shot Visual Question Answering	Jan 16, 2022	Language ModelingLanguage Modelling	—Unverified	0
MANGO: Enhancing the Robustness of VQA Models via Adversarial Noise Generation	Jan 16, 2022	Logical ReasoningQuestion Answering	—Unverified	0
All You May Need for VQA are Image Captions	Jan 16, 2022	AllImage Captioning	—Unverified	0
Task Formulation Matters When Learning Continuously: A Case Study in Visual Question Answering	Jan 16, 2022	Continual LearningIncremental Learning	—Unverified	0
Probing the Role of Positional Information in Vision-Language Models	Jan 16, 2022	Contrastive LearningImage-text matching	—Unverified	0
CLIP-TD: CLIP Targeted Distillation for Vision-Language Tasks	Jan 15, 2022	Question AnsweringVisual Commonsense Reasoning	—Unverified	0
A Thousand Words Are Worth More Than a Picture: Natural Language-Centric Outside-Knowledge Visual Question Answering	Jan 14, 2022	Generative Question AnsweringImage to text	—Unverified	0
Towards Automated Error Analysis: Learning to Characterize Errors	Jan 13, 2022	Common Sense ReasoningMeta-Learning	—Unverified	0
On the Efficacy of Co-Attention Transformer Layers in Visual Question Answering	Jan 11, 2022	POSQuestion Answering	—Unverified	0
Uni-EDEN: Universal Encoder-Decoder Network by Multi-Granular Vision-Language Pre-training	Jan 11, 2022	DecoderImage Captioning	—Unverified	0
COIN: Counterfactual Image Generation for VQA Interpretation	Jan 10, 2022	counterfactualImage Generation	—Unverified	0
FAVER: Blind Quality Prediction of Variable Frame Rate Videos	Jan 5, 2022	Cloud ComputingVideo Quality Assessment	CodeCode Available	1
Interactive Attention AI to translate low light photos to captions for night scene understanding in women safety	Jan 4, 2022	DecoderDeep Learning	—Unverified	0
V-Doc: Visual Questions Answers With Documents	Jan 1, 2022	Question AnsweringQuestion Generation	—Unverified	0
Transform-Retrieve-Generate: Natural Language-Centric Outside-Knowledge Visual Question Answering	Jan 1, 2022	Generative Question AnsweringImage to text	—Unverified	0
Query and Attention Augmentation for Knowledge-Based Explainable Reasoning	Jan 1, 2022	Question AnsweringVisual Question Answering	CodeCode Available	0
Towards General Purpose Vision Systems: An End-to-End Task-Agnostic Vision-Language Architecture	Jan 1, 2022	Question AnsweringVisual Question Answering	—Unverified	0
Maintaining Reasoning Consistency in Compositional Visual Question Answering	Jan 1, 2022	Question AnsweringVisual Question Answering	CodeCode Available	1
Multi-Image Visual Question Answering	Dec 27, 2021	Question AnsweringVisual Question Answering	CodeCode Available	0

Show:10 25 50

← PrevPage 52 of 87Next →

All datasets GQA Test2019 VQA v2 test-dev VQA v2 test-std OK-VQA MSVD-QA DocVQA test MSRVTT-QA InfographicVQA GQA test-dev VizWiz 2020 VQA A-OKVQA CLEVR

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	human	Accuracy	89.3	—	Unverified
2	DREAM+Unicoder-VL (MSRA)	Accuracy	76.04	—	Unverified
3	TRRNet (Ensemble)	Accuracy	74.03	—	Unverified
4	MIL-nbgao	Accuracy	73.81	—	Unverified
5	Kakao Brain	Accuracy	73.33	—	Unverified
6	Coarse-to-Fine Reasoning, Single Model	Accuracy	72.14	—	Unverified
7	270	Accuracy	70.23	—	Unverified
8	NSM ensemble (updated)	Accuracy	67.55	—	Unverified
9	VinVL-DPT	Accuracy	64.92	—	Unverified
10	VinVL+L	Accuracy	64.85	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	PaLI	Accuracy	84.3	—	Unverified
2	BEiT-3	Accuracy	84.19	—	Unverified
3	VLMo	Accuracy	82.78	—	Unverified
4	ONE-PEACE	Accuracy	82.6	—	Unverified
5	mPLUG (Huge)	Accuracy	82.43	—	Unverified
6	CuMo-7B	Accuracy	82.2	—	Unverified
7	X2-VLM (large)	Accuracy	81.9	—	Unverified
8	MMU	Accuracy	81.26	—	Unverified
9	Lyrics	Accuracy	81.2	—	Unverified
10	InternVL-C	Accuracy	81.2	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	BEiT-3	overall	84.03	—	Unverified
2	mPLUG-Huge	overall	83.62	—	Unverified
3	ONE-PEACE	overall	82.52	—	Unverified
4	X2-VLM (large)	overall	81.8	—	Unverified
5	VLMo	overall	81.3	—	Unverified
6	SimVLM	overall	80.34	—	Unverified
7	X2-VLM (base)	overall	80.2	—	Unverified
8	VAST	overall	80.19	—	Unverified
9	VALOR	overall	78.62	—	Unverified
10	Prompt Tuning	overall	78.53	—	Unverified