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 1021–1030 of 2167 papers

Title	Date	Tasks	Status	Hype
From Images to Textual Prompts: Zero-shot VQA with Frozen Large Language Models	Dec 21, 2022	Question AnsweringVisual Question Answering	CodeCode Available	0
DePlot: One-shot visual language reasoning by plot-to-table translation	Dec 20, 2022	Chart Question AnsweringFactual Inconsistency Detection in Chart Captioning	—Unverified	0
Towards Unsupervised Visual Reasoning: Do Off-The-Shelf Features Know How to Reason?	Dec 20, 2022	Question AnsweringRepresentation Learning	—Unverified	0
MIST: Multi-modal Iterative Spatial-Temporal Transformer for Long-form Video Question Answering	Dec 19, 2022	FormQuestion Answering	CodeCode Available	1
MatCha: Enhancing Visual Language Pretraining with Math Reasoning and Chart Derendering	Dec 19, 2022	Chart Question AnsweringData Summarization	—Unverified	0
SceneGATE: Scene-Graph based co-Attention networks for TExt visual question answering	Dec 16, 2022	Optical Character RecognitionOptical Character Recognition (OCR)	—Unverified	0
CLIPPO: Image-and-Language Understanding from Pixels Only	Dec 15, 2022	Contrastive Learningimage-classification	—Unverified	0
REVEAL: Retrieval-Augmented Visual-Language Pre-Training with Multi-Source Multimodal Knowledge Memory	Dec 10, 2022	Image CaptioningLanguage Modeling	CodeCode Available	0
VideoCoCa: Video-Text Modeling with Zero-Shot Transfer from Contrastive Captioners	Dec 9, 2022	Question AnsweringRetrieval	—Unverified	0
Harnessing the Power of Multi-Task Pretraining for Ground-Truth Level Natural Language Explanations	Dec 8, 2022	Explanation GenerationVisual Entailment	CodeCode Available	1

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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