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 1826–1850 of 2167 papers

Title	Date	Tasks	Status
Explainable High-order Visual Question Reasoning: A New Benchmark and Knowledge-routed Network	Sep 23, 2019	Question AnsweringTriplet	—Unverified
Triplet-Aware Scene Graph Embeddings	Sep 19, 2019	Data AugmentationGraph Embedding	—Unverified
Learning Sparse Mixture of Experts for Visual Question Answering	Sep 19, 2019	Mixture-of-ExpertsQuestion Answering	—Unverified
Inverse Visual Question Answering with Multi-Level Attentions	Sep 17, 2019	Question AnsweringVisual Question Answering	—Unverified
Sunny and Dark Outside?! Improving Answer Consistency in VQA through Entailed Question Generation	Sep 10, 2019	Common Sense ReasoningData Augmentation	—Unverified
Decoupled Box Proposal and Featurization with Ultrafine-Grained Semantic Labels Improve Image Captioning and Visual Question Answering	Sep 4, 2019	Image CaptioningObject	—Unverified
PlotQA: Reasoning over Scientific Plots	Sep 3, 2019	Chart Question AnsweringQuestion Answering	—Unverified
Adversarial Representation Learning for Text-to-Image Matching	Aug 28, 2019	Image CaptioningLanguage Modeling	—Unverified
Visual Question Answering using Deep Learning: A Survey and Performance Analysis	Aug 27, 2019	Common Sense ReasoningQuestion Answering	CodeCode Available
U-CAM: Visual Explanation using Uncertainty based Class Activation Maps	Aug 17, 2019	Deep LearningProbabilistic Deep Learning	—Unverified
What is needed for simple spatial language capabilities in VQA?	Aug 17, 2019	DiagnosticQuestion Answering	—Unverified
Language Features Matter: Effective Language Representations for Vision-Language Tasks	Aug 17, 2019	Image CaptioningLanguage Modelling	—Unverified
Reactive Multi-Stage Feature Fusion for Multimodal Dialogue Modeling	Aug 14, 2019	Question AnsweringScene-Aware Dialogue	—Unverified
Fusion of Detected Objects in Text for Visual Question Answering	Aug 14, 2019	Question AnsweringVisual Commonsense Reasoning	—Unverified
Multimodal Unified Attention Networks for Vision-and-Language Interactions	Aug 12, 2019	Question AnsweringVisual Grounding	—Unverified
Why Does a Visual Question Have Different Answers?	Aug 12, 2019	Question AnsweringVisual Question Answering	—Unverified
Multi-modality Latent Interaction Network for Visual Question Answering	Aug 10, 2019	Language ModelingLanguage Modelling	—Unverified
Question-Agnostic Attention for Visual Question Answering	Aug 9, 2019	Question AnsweringVisual Question Answering	—Unverified
CRIC: A VQA Dataset for Compositional Reasoning on Vision and Commonsense	Aug 8, 2019	Question AnsweringVisual Question Answering (VQA)	—Unverified
Answering Questions about Data Visualizations using Efficient Bimodal Fusion	Aug 5, 2019	Chart Question AnsweringOptical Character Recognition	CodeCode Available
The Meaning of ``Most'' for Visual Question Answering Models	Aug 1, 2019	Question AnsweringVisual Question Answering	—Unverified
An Empirical Study of Batch Normalization and Group Normalization in Conditional Computation	Jul 31, 2019	Conditional Image GenerationFew-Shot Learning	—Unverified
LEAF-QA: Locate, Encode & Attend for Figure Question Answering	Jul 30, 2019	Chart Question AnsweringQuestion Answering	—Unverified
An Empirical Study on Leveraging Scene Graphs for Visual Question Answering	Jul 28, 2019	Knowledge GraphsQuestion Answering	—Unverified
Bilinear Graph Networks for Visual Question Answering	Jul 23, 2019	Question AnsweringVisual Question Answering	—Unverified

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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