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 771–780 of 2167 papers

Title	Date	Tasks	Status	Hype
UNK-VQA: A Dataset and a Probe into the Abstention Ability of Multi-modal Large Models	Oct 17, 2023	AttributeQuestion Answering	CodeCode Available	0
VLIS: Unimodal Language Models Guide Multimodal Language Generation	Oct 15, 2023	Caption GenerationExplanation Generation	CodeCode Available	1
Exploring Sparse Spatial Relation in Graph Inference for Text-Based VQA	Oct 13, 2023	Graph LearningObject	—Unverified	0
PaLI-3 Vision Language Models: Smaller, Faster, Stronger	Oct 13, 2023	Chart Question AnsweringCross-Modal Retrieval	CodeCode Available	1
Open-Set Knowledge-Based Visual Question Answering with Inference Paths	Oct 12, 2023	Knowledge GraphsMulti-class Classification	CodeCode Available	0
Jaeger: A Concatenation-Based Multi-Transformer VQA Model	Oct 11, 2023	Dimensionality Reductionmodel	—Unverified	0
Off-Policy Evaluation for Human Feedback	Oct 11, 2023	Off-policy evaluationReinforcement Learning (RL)	—Unverified	0
Improving mitosis detection on histopathology images using large vision-language models	Oct 11, 2023	Domain GeneralizationImage Captioning	—Unverified	0
What If the TV Was Off? Examining Counterfactual Reasoning Abilities of Multi-modal Language Models	Oct 10, 2023	BenchmarkingCode Generation	CodeCode Available	1
How (not) to ensemble LVLMs for VQA	Oct 10, 2023	RetrievalVisual Question Answering (VQA)	—Unverified	0

Show:10 25 50

← PrevPage 78 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