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 841–850 of 2167 papers

Title	Date	Tasks	Status	Hype
Divide, Evaluate, and Refine: Evaluating and Improving Text-to-Image Alignment with Iterative VQA Feedback	Jul 10, 2023	Image GenerationVisual Question Answering (VQA)	—Unverified	0
Self-Adaptive Sampling for Efficient Video Question-Answering on Image--Text Models	Jul 9, 2023	Question AnsweringTGIF-Frame	CodeCode Available	1
GPT4RoI: Instruction Tuning Large Language Model on Region-of-Interest	Jul 7, 2023	AttributeCommon Sense Reasoning	CodeCode Available	2
UIT-Saviors at MEDVQA-GI 2023: Improving Multimodal Learning with Image Enhancement for Gastrointestinal Visual Question Answering	Jul 6, 2023	DiagnosticImage Enhancement	—Unverified	0
Localized Questions in Medical Visual Question Answering	Jul 3, 2023	Medical Visual Question AnsweringQuestion Answering	CodeCode Available	1
UniFine: A Unified and Fine-grained Approach for Zero-shot Vision-Language Understanding	Jul 3, 2023	Image-text matchingSentence	CodeCode Available	1
DoReMi: Grounding Language Model by Detecting and Recovering from Plan-Execution Misalignment	Jul 1, 2023	Language ModelingLanguage Modelling	—Unverified	0
Lightweight Recurrent Cross-modal Encoder for Video Question Answering	Jun 30, 2023	Action RecognitionQuestion Answering	CodeCode Available	0
Multimodal Prompt Retrieval for Generative Visual Question Answering	Jun 30, 2023	Domain AdaptationGenerative Visual Question Answering	CodeCode Available	1
CausalVLR: A Toolbox and Benchmark for Visual-Linguistic Causal Reasoning	Jun 30, 2023	Causal InferenceMedical Report Generation	CodeCode Available	3

Show:10 25 50

← PrevPage 85 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	InternVL-C	Accuracy	81.2	—	Unverified
10	Lyrics	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