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 1111–1120 of 2167 papers

Title	Date	Tasks	Status	Hype
LAVIS: A Library for Language-Vision Intelligence	Sep 15, 2022	BenchmarkingImage Captioning	—Unverified	0
OmniVL:One Foundation Model for Image-Language and Video-Language Tasks	Sep 15, 2022	Action ClassificationAction Recognition	—Unverified	0
PaLI: A Jointly-Scaled Multilingual Language-Image Model	Sep 14, 2022	DecoderFew-Shot Image Classification	—Unverified	0
Correlation Information Bottleneck: Towards Adapting Pretrained Multimodal Models for Robust Visual Question Answering	Sep 14, 2022	Adversarial RobustnessQuestion Answering	—Unverified	0
MUST-VQA: MUltilingual Scene-text VQA	Sep 14, 2022	Question AnsweringVisual Question Answering	—Unverified	0
PreSTU: Pre-Training for Scene-Text Understanding	Sep 12, 2022	DecoderImage Captioning	—Unverified	0
MaXM: Towards Multilingual Visual Question Answering	Sep 12, 2022	Question AnsweringTranslation	CodeCode Available	1
Pre-training image-language transformers for open-vocabulary tasks	Sep 9, 2022	Question AnsweringVisual Entailment	—Unverified	0
Improving the Cross-Lingual Generalisation in Visual Question Answering	Sep 7, 2022	Cross-Lingual TransferQuestion Answering	CodeCode Available	0
An Empirical Study of End-to-End Video-Language Transformers with Masked Visual Modeling	Sep 4, 2022	Fill MaskOptical Flow Estimation	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