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 691–700 of 2167 papers

Title	Date	Tasks	Status
BACON: Improving Clarity of Image Captions via Bag-of-Concept Graphs	Jul 3, 2024	Image CaptioningImage Generation	—Unverified
Data Augmentation for Visual Question Answering	Sep 1, 2017	Data AugmentationGeneral Classification	—Unverified
DARE: Diverse Visual Question Answering with Robustness Evaluation	Sep 26, 2024	image-classificationImage Classification	—Unverified
Backdooring Vision-Language Models with Out-Of-Distribution Data	Oct 2, 2024	Image CaptioningImage to text	—Unverified
A Comparative Evaluation of Temporal Pooling Methods for Blind Video Quality Assessment	Feb 25, 2020	Video Quality AssessmentVisual Question Answering (VQA)	—Unverified
Improving Automatic VQA Evaluation Using Large Language Models	Oct 4, 2023	In-Context LearningQuestion Answering	—Unverified
Improving mitosis detection on histopathology images using large vision-language models	Oct 11, 2023	Domain GeneralizationImage Captioning	—Unverified
Achieving Human Parity on Visual Question Answering	Nov 17, 2021	Question AnsweringVisual Question Answering	—Unverified
Analysis on Image Set Visual Question Answering	Mar 31, 2021	Question AnsweringVisual Question Answering	—Unverified
Image Semantic Relation Generation	Oct 19, 2022	Image RetrievalImage Segmentation	—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