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 751–760 of 2167 papers

Title	Date	Tasks	Status
Interpretable Visual Question Answering by Visual Grounding from Attention Supervision Mining	Aug 1, 2018	Question AnsweringVisual Grounding	—Unverified
Attention Mechanism based Cognition-level Scene Understanding	Apr 17, 2022	Question AnsweringScene Understanding	—Unverified
Prompting Medical Large Vision-Language Models to Diagnose Pathologies by Visual Question Answering	Jul 31, 2024	DiagnosticHallucination	—Unverified
Interpretable Visual Question Answering via Reasoning Supervision	Sep 7, 2023	Common Sense ReasoningQuestion Answering	—Unverified
Investigating Biases in Textual Entailment Datasets	Jun 23, 2019	BIG-bench Machine LearningNatural Language Inference	—Unverified
Iterated learning for emergent systematicity in VQA	May 3, 2021	Question AnsweringSystematic Generalization	—Unverified
Knowledge Acquisition for Visual Question Answering via Iterative Querying	Jul 1, 2017	Question AnsweringVisual Question Answering	—Unverified
Attention Guided Semantic Relationship Parsing for Visual Question Answering	Oct 5, 2020	ObjectQuestion Answering	—Unverified
Dynamic Clue Bottlenecks: Towards Interpretable-by-Design Visual Question Answering	May 24, 2023	Question AnsweringVisual Question Answering	—Unverified
Interpretable Counting for Visual Question Answering	Dec 23, 2017	Question AnsweringVisual Question 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