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 1301–1310 of 2167 papers

Title	Date	Tasks	Status	Hype
Does CLIP Benefit Visual Question Answering in the Medical Domain as Much as it Does in the General Domain?	Dec 27, 2021	ArticlesMedical Visual Question Answering	—Unverified	0
LaTr: Layout-Aware Transformer for Scene-Text VQA	Dec 23, 2021	Optical Character Recognition (OCR)Question Answering	CodeCode Available	1
Comprehensive Visual Question Answering on Point Clouds through Compositional Scene Manipulation	Dec 22, 2021	Common Sense ReasoningQuestion Answering	CodeCode Available	1
ScanQA: 3D Question Answering for Spatial Scene Understanding	Dec 20, 2021	3D Question Answering (3D-QA)Object	CodeCode Available	1
General Greedy De-bias Learning	Dec 20, 2021	image-classificationImage Classification	CodeCode Available	0
Task-Oriented Multi-User Semantic Communications	Dec 19, 2021	Image RetrievalMachine Translation	—Unverified	0
Zero-shot and Few-shot Learning with Knowledge Graphs: A Comprehensive Survey	Dec 18, 2021	Data AugmentationFew-Shot Learning	—Unverified	0
Understanding Attention for Vision-and-Language Tasks	Dec 17, 2021	Image GenerationImage Retrieval	—Unverified	0
Align and Prompt: Video-and-Language Pre-training with Entity Prompts	Dec 17, 2021	cross-modal alignmentEntity Alignment	CodeCode Available	1
KAT: A Knowledge Augmented Transformer for Vision-and-Language	Dec 16, 2021	Answer GenerationDecoder	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	InternVL-C	Accuracy	81.2	—	Unverified
10	Lyrics	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