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 1991–2000 of 2167 papers

Title	Date	Tasks	Status
Image as a Foreign Language: BEiT Pretraining for All Vision and Vision-Language Tasks	Aug 22, 2022	AllCross-Modal Retrieval	CodeCode Available
Illusory VQA: Benchmarking and Enhancing Multimodal Models on Visual Illusions	Dec 11, 2024	BenchmarkingQuestion Answering	CodeCode Available
Answer Them All! Toward Universal Visual Question Answering Models	Mar 1, 2019	AllQuestion Answering	CodeCode Available
Diversify, Rationalize, and Combine: Ensembling Multiple QA Strategies for Zero-shot Knowledge-based VQA	Jun 18, 2024	Question AnsweringVisual Question Answering	CodeCode Available
Benchmarking Multi-dimensional AIGC Video Quality Assessment: A Dataset and Unified Model	Jul 31, 2024	BenchmarkingLarge Language Model	CodeCode Available
Bayesian Low-Rank LeArning (Bella): A Practical Approach to Bayesian Neural Networks	Jul 30, 2024	Visual Question Answering (VQA)	CodeCode Available
Bilaterally Slimmable Transformer for Elastic and Efficient Visual Question Answering	Mar 24, 2022	GPUQuestion Answering	CodeCode Available
ILLUME: Rationalizing Vision-Language Models through Human Interactions	Aug 17, 2022	Image CaptioningQuestion Answering	CodeCode Available
Towards Efficient and Robust VQA-NLE Data Generation with Large Vision-Language Models	Sep 23, 2024	Decision MakingQuestion Answering	CodeCode Available
Barlow constrained optimization for Visual Question Answering	Mar 7, 2022	Question AnsweringVisual Question Answering	CodeCode Available

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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