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

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Showing 951–960 of 2167 papers

Title	Date	Tasks	Status	Score
End-to-End Audio Visual Scene-Aware Dialog using Multimodal Attention-Based Video Features	Jun 21, 2018	Question AnsweringVideo Description	CodeCode Available	5
Adversarial Training with OCR Modality Perturbation for Scene-Text Visual Question Answering	Mar 14, 2024	Optical Character RecognitionOptical Character Recognition (OCR)	CodeCode Available	5
LLM-Assisted Multi-Teacher Continual Learning for Visual Question Answering in Robotic Surgery	Feb 26, 2024	Continual LearningExemplar-Free	CodeCode Available	5
Differential Attention for Visual Question Answering	Apr 1, 2018	Question AnsweringVisual Question Answering	CodeCode Available	5
LMM-VQA: Advancing Video Quality Assessment with Large Multimodal Models	Aug 26, 2024	Large Language ModelVideo Quality Assessment	CodeCode Available	5
Locally Smoothed Neural Networks	Nov 22, 2017	Face VerificationQuestion Answering	CodeCode Available	5
Diffusion-Refined VQA Annotations for Semi-Supervised Gaze Following	Jun 4, 2024	Question AnsweringVisual Question Answering	CodeCode Available	5
ELIP: Efficient Language-Image Pre-training with Fewer Vision Tokens	Sep 28, 2023	Cross-Modal RetrievalGPU	CodeCode Available	5
Answering Questions about Data Visualizations using Efficient Bimodal Fusion	Aug 5, 2019	Chart Question AnsweringOptical Character Recognition	CodeCode Available	5
LININ: Logic Integrated Neural Inference Network for Explanatory Visual Question Answering	Dec 24, 2024	Explanatory Visual Question AnsweringMultimodal Reasoning	CodeCode Available	5

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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