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 926–950 of 2167 papers

Title	Date	Tasks	Status	Hype
A Symmetric Dual Encoding Dense Retrieval Framework for Knowledge-Intensive Visual Question Answering	Apr 26, 2023	DecoderKnowledge Distillation	CodeCode Available	1
Making Video Quality Assessment Models Robust to Bit Depth	Apr 25, 2023	Video Quality AssessmentVisual Question Answering (VQA)	—Unverified	0
HDR-ChipQA: No-Reference Quality Assessment on High Dynamic Range Videos	Apr 25, 2023	Video Quality AssessmentVisual Question Answering (VQA)	—Unverified	0
MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models	Apr 20, 2023	Image DescriptionLanguage Modelling	CodeCode Available	7
SurgicalGPT: End-to-End Language-Vision GPT for Visual Question Answering in Surgery	Apr 19, 2023	Question AnsweringScene Segmentation	CodeCode Available	1
Learning Situation Hyper-Graphs for Video Question Answering	Apr 18, 2023	DecoderQuestion Answering	CodeCode Available	1
VALOR: Vision-Audio-Language Omni-Perception Pretraining Model and Dataset	Apr 17, 2023	Audio captioningAudio-Video Question Answering (AVQA)	CodeCode Available	2
Perceptual Quality Assessment of Face Video Compression: A Benchmark and An Effective Method	Apr 14, 2023	Video CompressionVideo Quality Assessment	CodeCode Available	1
PDFVQA: A New Dataset for Real-World VQA on PDF Documents	Apr 13, 2023	document understandingKey Information Extraction	—Unverified	0
Zoom-VQA: Patches, Frames and Clips Integration for Video Quality Assessment	Apr 13, 2023	Video Quality AssessmentVisual Question Answering (VQA)	CodeCode Available	1
CAVL: Learning Contrastive and Adaptive Representations of Vision and Language	Apr 10, 2023	Image RetrievalPhrase Grounding	—Unverified	0
Improving Visual Question Answering Models through Robustness Analysis and In-Context Learning with a Chain of Basic Questions	Apr 6, 2023	In-Context LearningQuestion Answering	—Unverified	0
Q2ATransformer: Improving Medical VQA via an Answer Querying Decoder	Apr 4, 2023	ClassificationDecoder	—Unverified	0
Locate Then Generate: Bridging Vision and Language with Bounding Box for Scene-Text VQA	Apr 4, 2023	Answer GenerationLanguage Modelling	—Unverified	0
SC-ML: Self-supervised Counterfactual Metric Learning for Debiased Visual Question Answering	Apr 4, 2023	counterfactualMetric Learning	—Unverified	0
Instance-Level Trojan Attacks on Visual Question Answering via Adversarial Learning in Neuron Activation Space	Apr 2, 2023	Question AnsweringVisual Question Answering	—Unverified	0
MaMMUT: A Simple Architecture for Joint Learning for MultiModal Tasks	Mar 29, 2023	Cross-Modal RetrievalDecoder	CodeCode Available	0
LLaMA-Adapter: Efficient Fine-tuning of Language Models with Zero-init Attention	Mar 28, 2023	Instruction FollowingLanguage Modelling	CodeCode Available	5
Unmasked Teacher: Towards Training-Efficient Video Foundation Models	Mar 28, 2023	Action ClassificationAction Recognition	CodeCode Available	0
Curriculum Learning for Compositional Visual Reasoning	Mar 27, 2023	Question AnsweringVisual Question Answering	—Unverified	0
MD-VQA: Multi-Dimensional Quality Assessment for UGC Live Videos	Mar 27, 2023	Video Quality AssessmentVisual Question Answering (VQA)	CodeCode Available	1
Video-Text as Game Players: Hierarchical Banzhaf Interaction for Cross-Modal Representation Learning	Mar 25, 2023	Contrastive LearningQuestion Answering	CodeCode Available	1
MELTR: Meta Loss Transformer for Learning to Fine-tune Video Foundation Models	Mar 23, 2023	Auxiliary LearningMultimodal Sentiment Analysis	CodeCode Available	1
Top-Down Visual Attention from Analysis by Synthesis	Mar 23, 2023	RetrievalSemantic Segmentation	CodeCode Available	1
CoBIT: A Contrastive Bi-directional Image-Text Generation Model	Mar 23, 2023	DecoderImage Generation	—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