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 876–900 of 2167 papers

Title	Date	Tasks	Status	Hype
Revisiting the Role of Language Priors in Vision-Language Models	Jun 2, 2023	Image-text matchingImage-text Retrieval	CodeCode Available	1
Evaluating the Capabilities of Multi-modal Reasoning Models with Synthetic Task Data	Jun 1, 2023	Anomaly DetectionImage Generation	—Unverified	0
Layout and Task Aware Instruction Prompt for Zero-shot Document Image Question Answering	Jun 1, 2023	Optical Character Recognition (OCR)Question Answering	CodeCode Available	1
LiT-4-RSVQA: Lightweight Transformer-based Visual Question Answering in Remote Sensing	Jun 1, 2023	Question AnsweringVisual Question Answering	—Unverified	0
End-to-end Knowledge Retrieval with Multi-modal Queries	Jun 1, 2023	BenchmarkingCross-Modal Retrieval	CodeCode Available	1
Overcoming Language Bias in Remote Sensing Visual Question Answering via Adversarial Training	Jun 1, 2023	Question AnsweringVisual Question Answering	—Unverified	0
Using Visual Cropping to Enhance Fine-Detail Question Answering of BLIP-Family Models	May 31, 2023	Question AnsweringVisual Question Answering	—Unverified	0
Unveiling Cross Modality Bias in Visual Question Answering: A Causal View with Possible Worlds VQA	May 31, 2023	counterfactualCounterfactual Inference	—Unverified	0
Generate then Select: Open-ended Visual Question Answering Guided by World Knowledge	May 30, 2023	Answer SelectionQuestion Answering	—Unverified	0
VAST: A Vision-Audio-Subtitle-Text Omni-Modality Foundation Model and Dataset	May 29, 2023	Audio captioningAudio-Visual Captioning	CodeCode Available	2
PaLI-X: On Scaling up a Multilingual Vision and Language Model	May 29, 2023	Chart Question Answeringdocument understanding	CodeCode Available	1
HaVQA: A Dataset for Visual Question Answering and Multimodal Research in Hausa Language	May 28, 2023	Machine TranslationMultimodal Machine Translation	CodeCode Available	0
CrossGET: Cross-Guided Ensemble of Tokens for Accelerating Vision-Language Transformers	May 27, 2023	Image CaptioningImage Retrieval	CodeCode Available	1
Modularized Zero-shot VQA with Pre-trained Models	May 27, 2023	object-detectionObject Detection	CodeCode Available	0
Study of Subjective and Objective Quality Assessment of Mobile Cloud Gaming Videos	May 26, 2023	Video Quality AssessmentVisual Question Answering (VQA)	—Unverified	0
Zero-shot Visual Question Answering with Language Model Feedback	May 26, 2023	Language ModelingLanguage Modelling	CodeCode Available	0
Dynamic Clue Bottlenecks: Towards Interpretable-by-Design Visual Question Answering	May 24, 2023	Question AnsweringVisual Question Answering	—Unverified	0
NuScenes-QA: A Multi-modal Visual Question Answering Benchmark for Autonomous Driving Scenario	May 24, 2023	Autonomous DrivingQuestion Answering	CodeCode Available	2
Measuring Faithful and Plausible Visual Grounding in VQA	May 24, 2023	Question AnsweringVisual Grounding	CodeCode Available	0
Transferring Visual Attributes from Natural Language to Verified Image Generation	May 24, 2023	Image GenerationText to Image Generation	—Unverified	0
Image Manipulation via Multi-Hop Instructions -- A New Dataset and Weakly-Supervised Neuro-Symbolic Approach	May 23, 2023	Image ManipulationQuestion Answering	—Unverified	0
DUBLIN -- Document Understanding By Language-Image Network	May 23, 2023	Document Classificationdocument understanding	—Unverified	0
Getting ViT in Shape: Scaling Laws for Compute-Optimal Model Design	May 22, 2023	image-classificationImage Classification	—Unverified	0
VLAB: Enhancing Video Language Pre-training by Feature Adapting and Blending	May 22, 2023	Question AnsweringRetrieval	—Unverified	0
Towards Explainable In-the-Wild Video Quality Assessment: A Database and a Language-Prompted Approach	May 22, 2023	Video Quality AssessmentVisual Question Answering (VQA)	CodeCode Available	1

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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