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 421–430 of 2167 papers

Title	Date	Tasks	Status	Hype	Score
Learning to Answer Visual Questions from Web Videos	May 10, 2022	Dataset GenerationQuestion Answering	CodeCode Available	1	5
Disentangling 3D Prototypical Networks For Few-Shot Concept Learning	Nov 6, 2020	3D geometry3D Object Detection	CodeCode Available	1	5
IGLUE: A Benchmark for Transfer Learning across Modalities, Tasks, and Languages	Jan 27, 2022	Cross-Modal RetrievalFew-Shot Learning	CodeCode Available	1	5
IconQA: A New Benchmark for Abstract Diagram Understanding and Visual Language Reasoning	Oct 25, 2021	Arithmetic ReasoningMathematical Question Answering	CodeCode Available	1	5
Pano-AVQA: Grounded Audio-Visual Question Answering on 360^ Videos	Oct 11, 2021	Audio-visual Question AnsweringQuestion Answering	CodeCode Available	1	5
Panoramic Vision Transformer for Saliency Detection in 360° Videos	Sep 19, 2022	Saliency DetectionSaliency Prediction	CodeCode Available	1	5
Answer Mining from a Pool of Images: Towards Retrieval-Based Visual Question Answering	Jun 29, 2023	Answer GenerationQuestion Answering	CodeCode Available	1	5
Patch-VQ: 'Patching Up' the Video Quality Problem	Nov 27, 2020	Video Quality AssessmentVisual Question Answering (VQA)	CodeCode Available	1	5
Learning Situation Hyper-Graphs for Video Question Answering	Apr 18, 2023	DecoderQuestion Answering	CodeCode Available	1	5
Layout and Task Aware Instruction Prompt for Zero-shot Document Image Question Answering	Jun 1, 2023	Optical Character Recognition (OCR)Question Answering	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	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