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

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 131–140 of 2167 papers

Title	Date	Tasks	Status	Hype
Prophet: Prompting Large Language Models with Complementary Answer Heuristics for Knowledge-based Visual Question Answering	Mar 3, 2023	Language ModellingLarge Language Model	CodeCode Available	2
X^2-VLM: All-In-One Pre-trained Model For Vision-Language Tasks	Nov 22, 2022	AllCross-Modal Retrieval	CodeCode Available	2
Visual Programming: Compositional visual reasoning without training	Nov 18, 2022	In-Context LearningQuestion Answering	CodeCode Available	2
Exploring Video Quality Assessment on User Generated Contents from Aesthetic and Technical Perspectives	Nov 9, 2022	DisentanglementVideo Generation	CodeCode Available	2
PoseScript: Linking 3D Human Poses and Natural Language	Oct 21, 2022	Cross-Modal RetrievalImage Captioning	CodeCode Available	2
Neighbourhood Representative Sampling for Efficient End-to-end Video Quality Assessment	Oct 11, 2022	Video Quality AssessmentVisual Question Answering (VQA)	CodeCode Available	2
Pix2Struct: Screenshot Parsing as Pretraining for Visual Language Understanding	Oct 7, 2022	Chart Question AnsweringDiversity	CodeCode Available	2
Retrieval Augmented Visual Question Answering with Outside Knowledge	Oct 7, 2022	Answer GenerationDiagnostic	CodeCode Available	2
Learn to Explain: Multimodal Reasoning via Thought Chains for Science Question Answering	Sep 20, 2022	Multimodal Deep LearningMultimodal Reasoning	CodeCode Available	2
LST: Ladder Side-Tuning for Parameter and Memory Efficient Transfer Learning	Jun 13, 2022	Transfer LearningVisual Question Answering (VQA)	CodeCode Available	2

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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