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 1591–1600 of 2167 papers

Title	Date	Tasks	Status
Visual Entailment Task for Visually-Grounded Language Learning	Nov 26, 2018	Grounded language learningNatural Language Inference	—Unverified
Visual Explanations from Hadamard Product in Multimodal Deep Networks	Dec 18, 2017	Question AnsweringVisual Question Answering	—Unverified
Visual Fact Checker: Enabling High-Fidelity Detailed Caption Generation	Apr 30, 2024	Caption GenerationHallucination	—Unverified
Visual Graph Question Answering with ASP and LLMs for Language Parsing	Feb 13, 2025	Graph Question AnsweringOptical Character Recognition	—Unverified
Visual Grounding Strategies for Text-Only Natural Language Processing	Mar 25, 2021	Image RetrievalLanguage Modeling	—Unverified
Visual Hallucination: Definition, Quantification, and Prescriptive Remediations	Mar 26, 2024	HallucinationImage Captioning	—Unverified
Visually Guided Spatial Relation Extraction from Text	Jun 1, 2018	Activity RecognitionImage Captioning	—Unverified
Visual Mechanisms Inspired Efficient Transformers for Image and Video Quality Assessment	Mar 28, 2022	Image Quality AssessmentVideo Quality Assessment	—Unverified
Visual Perturbation-aware Collaborative Learning for Overcoming the Language Prior Problem	Jul 24, 2022	DiagnosticQuestion Answering	—Unverified
Visual Program Distillation: Distilling Tools and Programmatic Reasoning into Vision-Language Models	Dec 5, 2023	Language ModelingLanguage Modelling	—Unverified

Show:10 25 50

← PrevPage 160 of 217Next →

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