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 551–575 of 2167 papers

Title	Date	Tasks	Status	Hype	Score
Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering	Jul 25, 2017	Image CaptioningVisual Question Answering	CodeCode Available	1	5
DualVGR: A Dual-Visual Graph Reasoning Unit for Video Question Answering	Jul 10, 2021	Graph AttentionQuestion Answering	CodeCode Available	1	5
Evaluating Multimodal Representations on Visual Semantic Textual Similarity	Apr 4, 2020	BenchmarkingImage Captioning	CodeCode Available	1	5
Multimodal Federated Learning via Contrastive Representation Ensemble	Feb 17, 2023	Federated LearningImage-text Retrieval	CodeCode Available	1	5
End-to-end Document Recognition and Understanding with Dessurt	Mar 30, 2022	document understandingVisual Question Answering (VQA)	CodeCode Available	1	5
On the hidden treasure of dialog in video question answering	Mar 26, 2021	Question AnsweringVideo Question Answering	CodeCode Available	1	5
End-to-end Knowledge Retrieval with Multi-modal Queries	Jun 1, 2023	BenchmarkingCross-Modal Retrieval	CodeCode Available	1	5
Calibrating Concepts and Operations: Towards Symbolic Reasoning on Real Images	Oct 1, 2021	Question AnsweringVisual Question Answering	CodeCode Available	1	5
Large-Scale Adversarial Training for Vision-and-Language Representation Learning	Jun 11, 2020	Image-text RetrievalQuestion Answering	CodeCode Available	1	5
Graph Optimal Transport for Cross-Domain Alignment	Jun 26, 2020	Graph MatchingImage Captioning	CodeCode Available	1	5
MuKEA: Multimodal Knowledge Extraction and Accumulation for Knowledge-based Visual Question Answering	Mar 17, 2022	Implicit RelationsQuestion Answering	CodeCode Available	1	5
GraghVQA: Language-Guided Graph Neural Networks for Graph-based Visual Question Answering	Apr 20, 2021	Graph Neural NetworkGraph Question Answering	CodeCode Available	1	5
Boosting the Power of Small Multimodal Reasoning Models to Match Larger Models with Self-Consistency Training	Nov 23, 2023	Multimodal ReasoningScience Question Answering	CodeCode Available	1	5
Dual-Key Multimodal Backdoors for Visual Question Answering	Dec 14, 2021	Question AnsweringVisual Question Answering	CodeCode Available	1	5
Generic Attention-model Explainability for Interpreting Bi-Modal and Encoder-Decoder Transformers	Mar 29, 2021	DecoderImage Segmentation	CodeCode Available	1	5
Genixer: Empowering Multimodal Large Language Models as a Powerful Data Generator	Dec 11, 2023	Image CaptioningQuestion Answering	CodeCode Available	1	5
Multi-Agent VQA: Exploring Multi-Agent Foundation Models in Zero-Shot Visual Question Answering	Mar 21, 2024	object-detectionObject Detection	CodeCode Available	1	5
eP-ALM: Efficient Perceptual Augmentation of Language Models	Mar 20, 2023	In-Context LearningVisual Question Answering (VQA)	CodeCode Available	1	5
ERNIE-Layout: Layout Knowledge Enhanced Pre-training for Visually-rich Document Understanding	Oct 12, 2022	document-image-classificationDocument Image Classification	CodeCode Available	1	5
Going Full-TILT Boogie on Document Understanding with Text-Image-Layout Transformer	Feb 18, 2021	DecoderDocument Image Classification	CodeCode Available	1	5
Exploring Opinion-unaware Video Quality Assessment with Semantic Affinity Criterion	Feb 26, 2023	Video Quality AssessmentVisual Question Answering (VQA)	CodeCode Available	1	5
Enhancing Visual Question Answering through Question-Driven Image Captions as Prompts	Apr 12, 2024	Image CaptioningQuestion Answering	CodeCode Available	1	5
Evaluating Image Hallucination in Text-to-Image Generation with Question-Answering	Sep 19, 2024	HallucinationHallucination Evaluation	CodeCode Available	1	5
GQA: A New Dataset for Real-World Visual Reasoning and Compositional Question Answering	Feb 25, 2019	Question AnsweringVisual Question Answering (VQA)	CodeCode Available	1	5
Florence: A New Foundation Model for Computer Vision	Nov 22, 2021	Action ClassificationAction Recognition	CodeCode Available	1	5

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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