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

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Showing 1351–1360 of 2167 papers

Title	Date	Tasks	Status
Correlation Information Bottleneck: Towards Adapting Pretrained Multimodal Models for Robust Visual Question Answering	Sep 14, 2022	Adversarial RobustnessQuestion Answering	—Unverified
PreSTU: Pre-Training for Scene-Text Understanding	Sep 12, 2022	DecoderImage Captioning	—Unverified
Pre-training image-language transformers for open-vocabulary tasks	Sep 9, 2022	Question AnsweringVisual Entailment	—Unverified
Improving the Cross-Lingual Generalisation in Visual Question Answering	Sep 7, 2022	Cross-Lingual TransferQuestion Answering	CodeCode Available
Evaluating Point Cloud from Moving Camera Videos: A No-Reference Metric	Aug 30, 2022	Image Quality AssessmentPoint Cloud Quality Assessment	CodeCode Available
Bidirectional Contrastive Split Learning for Visual Question Answering	Aug 24, 2022	Adversarial AttackBackdoor Attack	—Unverified
FashionVQA: A Domain-Specific Visual Question Answering System	Aug 24, 2022	Question AnsweringVisual Question Answering	—Unverified
How good are deep models in understanding the generated images?	Aug 23, 2022	ObjectObject Recognition	—Unverified
Image as a Foreign Language: BEiT Pretraining for All Vision and Vision-Language Tasks	Aug 22, 2022	AllCross-Modal Retrieval	CodeCode Available
VLMAE: Vision-Language Masked Autoencoder	Aug 19, 2022	Image-text RetrievalLanguage Modeling	—Unverified

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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