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 1761–1770 of 2167 papers

Title	Date	Tasks	Status
VTQA: Visual Text Question Answering via Entity Alignment and Cross-Media Reasoning	Mar 5, 2023	Answer GenerationEntity Alignment	CodeCode Available
End-to-End Audio Visual Scene-Aware Dialog using Multimodal Attention-Based Video Features	Jun 21, 2018	Question AnsweringVideo Description	CodeCode Available
Modularized Zero-shot VQA with Pre-trained Models	May 27, 2023	object-detectionObject Detection	CodeCode Available
NeSyCoCo: A Neuro-Symbolic Concept Composer for Compositional Generalization	Dec 20, 2024	Compositional Generalization (AVG)Novel Concepts	CodeCode Available
ELIP: Efficient Language-Image Pre-training with Fewer Vision Tokens	Sep 28, 2023	Cross-Modal RetrievalGPU	CodeCode Available
Co-attending Free-form Regions and Detections with Multi-modal Multiplicative Feature Embedding for Visual Question Answering	Nov 18, 2017	FormVisual Question Answering	CodeCode Available
Neural Module Networks	Nov 9, 2015	Visual Question AnsweringVisual Question Answering (VQA)	CodeCode Available
Effective Approaches to Batch Parallelization for Dynamic Neural Network Architectures	Jul 8, 2017	Mixture-of-ExpertsQuestion Answering	CodeCode Available
Neural-Symbolic VQA: Disentangling Reasoning from Vision and Language Understanding	Oct 4, 2018	Question AnsweringRepresentation Learning	CodeCode Available
CLIPVQA:Video Quality Assessment via CLIP	Jul 6, 2024	Video Quality AssessmentVisual Question Answering (VQA)	CodeCode Available

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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	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