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 1241–1250 of 2167 papers

Title	Date	Tasks	Status
OnlineVPO: Align Video Diffusion Model with Online Video-Centric Preference Optimization	Dec 19, 2024	Video Quality AssessmentVisual Question Answering (VQA)	—Unverified
On the Cognition of Visual Question Answering Models and Human Intelligence: A Comparative Study	Oct 4, 2023	Question AnsweringVisual Question Answering	—Unverified
On the Effects of Video Grounding on Language Models	Oct 1, 2022	Image CaptioningQuestion Answering	—Unverified
On the Efficacy of Co-Attention Transformer Layers in Visual Question Answering	Jan 11, 2022	POSQuestion Answering	—Unverified
On the Flip Side: Identifying Counterexamples in Visual Question Answering	Jun 3, 2018	Question AnsweringVisual Question Answering	—Unverified
On the General Value of Evidence, and Bilingual Scene-Text Visual Question Answering	Feb 24, 2020	Question AnsweringReferring Expression	—Unverified
On the Robustness of Large Multimodal Models Against Image Adversarial Attacks	Dec 6, 2023	Image Captioningimage-classification	—Unverified
On the Role of Visual Grounding in VQA	Jun 26, 2024	Visual GroundingVisual Question Answering (VQA)	—Unverified
On the Significance of Question Encoder Sequence Model in the Out-of-Distribution Performance in Visual Question Answering	Aug 28, 2021	Graph AttentionQuestion Answering	—Unverified
On the Value of Out-of-Distribution Testing: An Example of Goodhart's Law	May 19, 2020	Model SelectionQuestion Answering	—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