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
A survey on VQA_Datasets and Approaches	May 2, 2021	Question AnsweringSurvey	—Unverified
Improving and Diagnosing Knowledge-Based Visual Question Answering via Entity Enhanced Knowledge Injection	Dec 13, 2021	Common Sense ReasoningKnowledge Graph Embeddings	—Unverified
Improving Automatic VQA Evaluation Using Large Language Models	Oct 4, 2023	In-Context LearningQuestion Answering	—Unverified
Improving Cross-Modal Understanding in Visual Dialog via Contrastive Learning	Apr 15, 2022	Contrastive LearningQuestion Answering	—Unverified
Improving Data Augmentation for Robust Visual Question Answering with Effective Curriculum Learning	Jan 28, 2024	Data AugmentationQuestion Answering	—Unverified
Improving Generalization in Visual Reasoning via Self-Ensemble	Oct 28, 2024	Visual Question Answering (VQA)Visual Reasoning	—Unverified
A survey on knowledge-enhanced multimodal learning	Nov 19, 2022	Conditional Image GenerationFactual Visual Question Answering	—Unverified
Improving mitosis detection on histopathology images using large vision-language models	Oct 11, 2023	Domain GeneralizationImage Captioning	—Unverified
Graph-based Heuristic Search for Module Selection Procedure in Neural Module Network	Sep 30, 2020	Heuristic SearchQuestion Answering	—Unverified
GRAM: Global Reasoning for Multi-Page VQA	Jan 7, 2024	Question AnsweringVisual Question Answering	—Unverified

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	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