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 1311–1320 of 2167 papers

Title	Date	Tasks	Status	Hype
Distilled Dual-Encoder Model for Vision-Language Understanding	Dec 16, 2021	Image to textmodel	CodeCode Available	1
3D Question Answering	Dec 15, 2021	3D geometryQuestion Answering	—Unverified	0
Bilateral Cross-Modality Graph Matching Attention for Feature Fusion in Visual Question Answering	Dec 14, 2021	Graph MatchingQuestion Answering	CodeCode Available	1
Dual-Key Multimodal Backdoors for Visual Question Answering	Dec 14, 2021	Question AnsweringVisual Question Answering	CodeCode Available	1
Improving and Diagnosing Knowledge-Based Visual Question Answering via Entity Enhanced Knowledge Injection	Dec 13, 2021	Common Sense ReasoningKnowledge Graph Embeddings	—Unverified	0
Video as Conditional Graph Hierarchy for Multi-Granular Question Answering	Dec 12, 2021	Question AnsweringVideo Question Answering	CodeCode Available	1
Change Detection Meets Visual Question Answering	Dec 12, 2021	Answer GenerationChange Detection	CodeCode Available	1
Unified Multimodal Pre-training and Prompt-based Tuning for Vision-Language Understanding and Generation	Dec 10, 2021	Image-text matchingImage-text Retrieval	—Unverified	0
MLP Architectures for Vision-and-Language Modeling: An Empirical Study	Dec 8, 2021	Language ModelingLanguage Modelling	CodeCode Available	1
MoCA: Incorporating Multi-stage Domain Pretraining and Cross-guided Multimodal Attention for Textbook Question Answering	Dec 6, 2021	Language ModellingQuestion Answering	—Unverified	0

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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