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 1531–1540 of 2167 papers

Title	Date	Tasks	Status
Semantically Distributed Robust Optimization for Vision-and-Language Inference	Oct 14, 2021	Data AugmentationNatural Language Inference	CodeCode Available
Improving Users' Mental Model with Attention-directed Counterfactual Edits	Oct 13, 2021	counterfactualQuestion Answering	—Unverified
MMIU: Dataset for Visual Intent Understanding in Multimodal Assistants	Oct 13, 2021	intent-classificationIntent Classification	—Unverified
Beyond Accuracy: A Consolidated Tool for Visual Question Answering Benchmarking	Oct 11, 2021	BenchmarkingQuestion Answering	CodeCode Available
Asking questions on handwritten document collections	Oct 2, 2021	Optical Character Recognition (OCR)Question Answering	—Unverified
Breaking Down Questions for Outside-Knowledge VQA	Sep 29, 2021	Graph Neural NetworkQuestion Answering	—Unverified
PRNet: A Progressive Regression Network for No-Reference User-Generated-Content Video Quality Assessment	Sep 29, 2021	regressionVideo Quality Assessment	—Unverified
Crossformer: Transformer with Alternated Cross-Layer Guidance	Sep 29, 2021	Inductive BiasMachine Translation	—Unverified
How Much Can CLIP Benefit Vision-and-Language Tasks?	Sep 29, 2021	Question AnsweringVisual Entailment	—Unverified
Variational Disentangled Attention for Regularized Visual Dialog	Sep 29, 2021	Question AnsweringVisual Dialog	—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