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 726–750 of 2167 papers

Title	Date	Tasks	Status	Hype
Fully Authentic Visual Question Answering Dataset from Online Communities	Nov 27, 2023	Question AnsweringVisual Question Answering	CodeCode Available	0
How Many Unicorns Are in This Image? A Safety Evaluation Benchmark for Vision LLMs	Nov 27, 2023	Adversarial RobustnessVisual Question Answering (VQA)	CodeCode Available	1
Boosting the Power of Small Multimodal Reasoning Models to Match Larger Models with Self-Consistency Training	Nov 23, 2023	Multimodal ReasoningScience Question Answering	CodeCode Available	1
From Wrong To Right: A Recursive Approach Towards Vision-Language Explanation	Nov 21, 2023	Explanation GenerationVisual Question Answering (VQA)	—Unverified	0
KNVQA: A Benchmark for evaluation knowledge-based VQA	Nov 21, 2023	HallucinationObject Hallucination	—Unverified	0
Filling the Image Information Gap for VQA: Prompting Large Language Models to Proactively Ask Questions	Nov 20, 2023	Question AnsweringVisual Question Answering	CodeCode Available	0
Understanding and Mitigating Classification Errors Through Interpretable Token Patterns	Nov 18, 2023	ClassificationNER	—Unverified	0
HIDRO-VQA: High Dynamic Range Oracle for Video Quality Assessment	Nov 18, 2023	Video Quality AssessmentVisual Question Answering (VQA)	CodeCode Available	1
Video-LLaVA: Learning United Visual Representation by Alignment Before Projection	Nov 16, 2023	Language ModelingLanguage Modelling	CodeCode Available	4
Multiple-Question Multiple-Answer Text-VQA	Nov 15, 2023	DecoderDenoising	—Unverified	0
Improving Zero-shot Visual Question Answering via Large Language Models with Reasoning Question Prompts	Nov 15, 2023	Question AnsweringSentence	CodeCode Available	0
Attribute Diversity Determines the Systematicity Gap in VQA	Nov 15, 2023	AttributeDiagnostic	CodeCode Available	0
Asking More Informative Questions for Grounded Retrieval	Nov 14, 2023	Question AnsweringQuestion Selection	—Unverified	0
A Comprehensive Evaluation of GPT-4V on Knowledge-Intensive Visual Question Answering	Nov 13, 2023	Decision MakingExplanation Generation	CodeCode Available	1
CLiF-VQA: Enhancing Video Quality Assessment by Incorporating High-Level Semantic Information related to Human Feelings	Nov 13, 2023	Video Quality AssessmentVisual Question Answering (VQA)	—Unverified	0
What Large Language Models Bring to Text-rich VQA?	Nov 13, 2023	Image ComprehensionOptical Character Recognition (OCR)	—Unverified	0
SPHINX: The Joint Mixing of Weights, Tasks, and Visual Embeddings for Multi-modal Large Language Models	Nov 13, 2023	Described Object DetectionLanguage Modeling	CodeCode Available	4
InfMLLM: A Unified Framework for Visual-Language Tasks	Nov 12, 2023	GPUImage Captioning	CodeCode Available	1
Visual Commonsense based Heterogeneous Graph Contrastive Learning	Nov 11, 2023	Contrastive LearningQuestion Answering	—Unverified	0
Monkey: Image Resolution and Text Label Are Important Things for Large Multi-modal Models	Nov 11, 2023	Image CaptioningMMR total	CodeCode Available	3
Analyzing Modular Approaches for Visual Question Decomposition	Nov 10, 2023	Code GenerationVisual Question Answering (VQA)	CodeCode Available	0
Improving Vision-and-Language Reasoning via Spatial Relations Modeling	Nov 9, 2023	Position regressionRelation	—Unverified	0
Zero-shot Translation of Attention Patterns in VQA Models to Natural Language	Nov 8, 2023	Image CaptioningLanguage Modeling	CodeCode Available	0
mPLUG-Owl2: Revolutionizing Multi-modal Large Language Model with Modality Collaboration	Nov 7, 2023	1 Image, 2*2 StitchingDecoder	CodeCode Available	4
CogVLM: Visual Expert for Pretrained Language Models	Nov 6, 2023	1 Image, 2*2 StitchingFS-MEVQA	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	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