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 1341–1350 of 2167 papers

Title	Date	Tasks	Status
Reducing Language Biases in Visual Question Answering with Visually-Grounded Question Encoder	Jul 13, 2020	Question AnsweringVisual Grounding	—Unverified
Regularizing Attention Networks for Anomaly Detection in Visual Question Answering	Sep 21, 2020	Anomaly DetectionQuestion Answering	—Unverified
Reka Core, Flash, and Edge: A Series of Powerful Multimodal Language Models	Apr 18, 2024	GSM8KMMLU	—Unverified
Rephrasing visual questions by specifying the entropy of the answer distribution	Apr 10, 2020	Question AnsweringVisual Question Answering	—Unverified
Representation, Learning and Reasoning on Spatial Language for Downstream NLP Tasks	Nov 1, 2020	Common Sense ReasoningQuestion Answering	—Unverified
Representing Movie Characters in Dialogues	Nov 1, 2019	Question AnsweringRelation Classification	—Unverified
Reproducibility Report for "Learning To Count Objects In Natural Images For Visual Question Answering"	May 21, 2018	Question AnsweringVisual Question Answering	—Unverified
RepsNet: Combining Vision with Language for Automated Medical Reports	Sep 27, 2022	Contrastive LearningDecoder	—Unverified
RescueADI: Adaptive Disaster Interpretation in Remote Sensing Images with Autonomous Agents	Oct 17, 2024	Question AnsweringTask Planning	—Unverified
Reassessing Evaluation Practices in Visual Question Answering: A Case Study on Out-of-Distribution Generalization	May 24, 2022	Image CaptioningOut-of-Distribution Generalization	—Unverified

Show:10 25 50

← PrevPage 135 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