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 1776–1800 of 2167 papers

Title	Date	Tasks	Status	Hype
TAB-VCR: Tags and Attributes based Visual Commonsense Reasoning Baselines	Oct 31, 2019	AttributeQuestion Answering	CodeCode Available	0
C3DVQA: Full-Reference Video Quality Assessment with 3D Convolutional Neural Network	Oct 30, 2019	Video Quality AssessmentVisual Question Answering (VQA)	—Unverified	0
Learning Rich Image Region Representation for Visual Question Answering	Oct 29, 2019	Language ModelingLanguage Modelling	—Unverified	0
KnowIT VQA: Answering Knowledge-Based Questions about Videos	Oct 23, 2019	Question AnsweringVideo Question Answering	—Unverified	0
Enforcing Reasoning in Visual Commonsense Reasoning	Oct 21, 2019	Question AnsweringReinforcement Learning	—Unverified	0
Good, Better, Best: Textual Distractors Generation for Multiple-Choice Visual Question Answering via Reinforcement Learning	Oct 21, 2019	Data AugmentationDecision Making	—Unverified	0
Neural Memory Plasticity for Anomaly Detection	Oct 12, 2019	Anomaly DetectionEEG	—Unverified	0
Multi-modal Deep Analysis for Multimedia	Oct 11, 2019	Multi-modal RecommendationQuestion Answering	—Unverified	0
Modulated Self-attention Convolutional Network for VQA	Oct 8, 2019	Question AnsweringVisual Question Answering	—Unverified	0
REMIND Your Neural Network to Prevent Catastrophic Forgetting	Oct 6, 2019	QuantizationQuestion Answering	CodeCode Available	0
SegEQA: Video Segmentation Based Visual Attention for Embodied Question Answering	Oct 1, 2019	Embodied Question AnsweringQuestion Answering	—Unverified	0
From Strings to Things: Knowledge-Enabled VQA Model That Can Read and Reason	Oct 1, 2019	Graph Neural NetworkQuestion Answering	—Unverified	0
On Incorporating Semantic Prior Knowledge in Deep Learning Through Embedding-Space Constraints	Sep 30, 2019	Data AugmentationQuestion Answering	—Unverified	0
Compact Trilinear Interaction for Visual Question Answering	Sep 26, 2019	BenchmarkingKnowledge Distillation	CodeCode Available	0
Overcoming Data Limitation in Medical Visual Question Answering	Sep 26, 2019	DenoisingMedical Visual Question Answering	CodeCode Available	1
UNITER: Learning UNiversal Image-TExt Representations	Sep 25, 2019	Image-text matchingImage-text Retrieval	—Unverified	0
Learning to Recognize the Unseen Visual Predicates	Sep 25, 2019	Question AnsweringVisual Question Answering	—Unverified	0
Why Does the VQA Model Answer No?: Improving Reasoning through Visual and Linguistic Inference	Sep 25, 2019	Common Sense ReasoningQuestion Answering	—Unverified	0
On Incorporating Semantic Prior Knowlegde in Deep Learning Through Embedding-Space Constraints	Sep 25, 2019	Data AugmentationQuestion Answering	—Unverified	0
UNITER: UNiversal Image-TExt Representation Learning	Sep 25, 2019	Image-text matchingImage-text Retrieval	CodeCode Available	1
Unified Vision-Language Pre-Training for Image Captioning and VQA	Sep 24, 2019	DecoderImage Captioning	CodeCode Available	2
Non-monotonic Logical Reasoning Guiding Deep Learning for Explainable Visual Question Answering	Sep 23, 2019	Inductive LearningLogical Reasoning	—Unverified	0
Explainable High-order Visual Question Reasoning: A New Benchmark and Knowledge-routed Network	Sep 23, 2019	Question AnsweringTriplet	—Unverified	0
Learning Sparse Mixture of Experts for Visual Question Answering	Sep 19, 2019	Mixture-of-ExpertsQuestion Answering	—Unverified	0
Triplet-Aware Scene Graph Embeddings	Sep 19, 2019	Data AugmentationGraph Embedding	—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